Cancer diagnostics using non-coding transcripts

ABSTRACT

Disclosed herein, in certain instances, are methods for the diagnosis, prognosis and determination of cancer progression of a cancer in a subject. Further disclosed herein, in certain instances, are methods for determining the treatment modality of a cancer in a subject. The methods comprise expression-based analysis of non-coding targets and coding targets. Further disclosed herein, in certain instances, are probe sets for use in assessing a cancer status in a subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a 35 USC § 371 National Stage application of International Application No. PCT/US2012/069571 filed Dec. 13, 2012; which claims the benefit under 35 USC § 119(e) to U.S. Application Ser. No. 61/703,426 filed Nov. 27, 2012, U.S. Application Ser. No. 61/652,044 filed May 25, 2012, U.S. Application Ser. No. 61/570,194 filed Dec. 13, 2011. The disclosure of each of the prior applications is considered part of and is incorporated by reference in the disclosure of this application.

This application claims benefit of priority under 35 U.S.C. § 119(e) from U.S. Provisional Patent Application No. 61/570,194, filed Dec. 13, 2011, U.S. Provisional Patent Application No. 61/652,044, filed May 25, 2012, and U.S. Provisional Patent Application No. 61/730,426, filed Nov. 27, 2012, which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

Cancer is the uncontrolled growth of abnormal cells anywhere in a body. The abnormal cells are termed cancer cells, malignant cells, or tumor cells. Many cancers and the abnormal cells that compose the cancer tissue are further identified by the name of the tissue that the abnormal cells originated from (for example, breast cancer, lung cancer, colon cancer, prostate cancer, pancreatic cancer, thyroid cancer). Cancer is not confined to humans; animals and other living organisms can get cancer. Cancer cells can proliferate uncontrollably and form a mass of cancer cells. Cancer cells can break away from this original mass of cells, travel through the blood and lymph systems, and lodge in other organs where they can again repeat the uncontrolled growth cycle. This process of cancer cells leaving an area and growing in another body area is often termed metastatic spread or metastatic disease. For example, if breast cancer cells spread to a bone (or anywhere else), it can mean that the individual has metastatic breast cancer.

Standard clinical parameters such as tumor size, grade, lymph node involvement and tumor-node-metastasis (TNM) staging (American Joint Committee on Cancer at the world wide web at cancerstaging.org) may correlate with outcome and serve to stratify patients with respect to (neo)adjuvant chemotherapy, immunotherapy, antibody therapy and/or radiotherapy regimens. Incorporation of molecular markers in clinical practice may define tumor subtypes that are more likely to respond to targeted therapy. However, stage-matched tumors grouped by histological or molecular subtypes may respond differently to the same treatment regimen. Additional key genetic and epigenetic alterations may exist with important etiological contributions. A more detailed understanding of the molecular mechanisms and regulatory pathways at work in cancer cells and the tumor microenvironment (TME) could dramatically improve the design of novel anti-tumor drugs and inform the selection of optimal therapeutic strategies. The development and implementation of diagnostic, prognostic and therapeutic biomarkers to characterize the biology of each tumor may assist clinicians in making important decisions with regard to individual patient care and treatment. Thus, disclosed herein are methods, compositions and systems for the analysis of coding and/or non-coding targets for the diagnosis, prognosis, and monitoring of a cancer.

This background information is provided for the purpose of making known information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.

SUMMARY OF THE INVENTION

To aid in the understanding of the present invention, a list of commonly used abbreviations is provided in Table 1. Disclosed herein are compositions, systems, and methods for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject. In some instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is a non-coding RNA transcript selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

In some instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is not selected from the group consisting of a miRNA and an intronic sequence; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

Alternatively, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is not selected from the group consisting of a miRNA, an intronic sequence, and a UTR sequence; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

In other instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets consist essentially of a non-coding target or a non-exonic transcript; (ii) the non-coding target is selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, and (iii) the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the method further comprises assaying an expression level of a coding target.

In some instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the method further comprises assaying an expression level of a coding target.

Alternatively, the method comprises (a) providing a sample from a subject; (b) conducting a reaction to determine an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets are identified based on a classifier; and (c) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

The method may comprise (a) providing a sample from a subject; (b) conducting a reaction to determine an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets are identified based on at least one probe selection region (PSR); and (c) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

In other instances, the method comprises (a) providing a sample from a subject; (b) conducting a reaction to determine an expression level in a sample from the subject for a plurality of targets, wherein at least about 10% of the plurality of targets are non-coding targets; and (c) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

Further disclosed herein in some embodiments is a method of analyzing a cancer in an individual in need thereof, comprising: (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; and (b) comparing the expression profile from the sample to an expression profile of a control or standard. In some embodiments, the method further comprises providing diagnostic or prognostic information to the individual about the cardiovascular disorder based on the comparison.

Further disclosed herein in some embodiments is a method of diagnosing cancer in an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) diagnosing a cancer in the individual if the expression profile of the sample (i) deviates from the control or standard from a healthy individual or population of healthy individuals, or (ii) matches the control or standard from an individual or population of individuals who have or have had the cancer.

Further disclosed herein in some embodiments is a method of predicting whether an individual is susceptible to developing a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the susceptibility of the individual for developing a cancer based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Further disclosed herein in some embodiments is a method of predicting an individual's response to a treatment regimen for a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the individual's response to a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Disclosed herein in some embodiments is a method of prescribing a treatment regimen for a cancer to an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) prescribing a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some embodiments, the methods disclosed herein further comprise diagnosing the individual with a cancer if the expression profile of the sample (a) deviates from the control or standard from a healthy individual or population of healthy individuals, or (b) matches the control or standard from an individual or population of individuals who have or have had the cancer.

The methods disclosed herein can further comprise predicting the susceptibility of the individual for developing a cancer based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. In some instances, the methods disclosed herein further comprise prescribing a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. Alternatively, or additionally, the methods disclosed herein further comprise altering a treatment regimen prescribed or administered to the individual based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some instances, the methods disclosed herein further comprise predicting the individual's response to a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. In some instances, the deviation is the expression level of one or more targets from the sample is greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. Alternatively, or additionally, the deviation is the expression level of one or more targets from the sample is at least about 30% greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. In some embodiments, the deviation is the expression level of one or more targets from the sample is less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. In some instances, the deviation is the expression level of one or more targets from the sample is at least about 30% less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals.

The methods disclosed herein can further comprise using a machine to isolate the target or the probe from the sample. Alternatively, or additionally, the methods disclosed herein further comprise contacting the sample with a label that specifically binds to the target, the probe, or a combination thereof. In some embodiments, the methods disclosed herein further comprise contacting the sample with a label that specifically binds to a target selected from Table 6. In some embodiments, the methods disclosed herein further comprise amplifying the target, the probe, or any combination thereof. The methods disclosed herein can further comprise sequencing the target, the probe, or any combination thereof. In some instances, the method further comprises quantifying the expression level of the plurality of targets. In some embodiments, the method further comprises labeling the plurality of targets.

In some instances, the methods disclosed herein further comprise converting the expression levels of the target sequences into a likelihood score that indicates the probability that a biological sample is from a patient who will a clinical outcome. In some instances, the clinical outcome is an exhibition of: (a) no evidence of disease; (b) no disease progression; (c) disease progression; (d) metastasis; (e) no metastasis; (f) systemic cancer; or (g) biochemical recurrence.

In some embodiments, the methods disclosed herein further comprise quantifying the expression level of the plurality of targets. In some instances, the method further comprises labeling the plurality of targets. In some instances, the target sequences are differentially expressed in the cancer. In some embodiments, the differential expression is dependent on aggressiveness. The expression profile can be determined by a method selected from the group consisting of RT-PCR, Northern blotting, ligase chain reaction, array hybridization, and a combination thereof. Alternatively, the expression profile is determined by RNA-Seq.

In some instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 50%. In other instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 60%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 65%. Alternatively, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 70%. In some instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 75%. In other instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 80%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 85%. Alternatively, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 90%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 95%.

In some instances, assaying the expression level of a plurality of targets comprises the use of a probe set. Assaying the expression level of a plurality of targets can comprise the use of a probe selection region (PSR). Alternatively, or additionally, assaying the expression level of a plurality of targets can comprise the use of an ICE block. In some embodiments, obtaining the expression level comprises the use of a classifier. The classifier may comprise a probe selection region (PSR). In some instances, the classifier comprises the use of an algorithm. The algorithm can comprise a machine learning algorithm. In some instances, obtaining the expression level also comprise sequencing the plurality of targets. In some embodiments, obtaining the expression level may also comprise amplifying the plurality of targets. In some embodiments, obtaining the expression level may also comprise quantifying the plurality of targets.

In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy or malignant potential of the cancer or tumor. Alternatively, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the stage of the cancer. The diagnosing, predicting, and/or monitoring the status or outcome of a cancer can comprise determining the tumor grade. Alternatively, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises assessing the risk of developing a cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment.

In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein is a kit for analyzing a cancer, comprising (a) a probe set comprising a plurality of target sequences, wherein the plurality of target sequences comprises at least one target sequence listed in Table 6; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target sequences in a sample. In some embodiments, the kit further comprises a computer model or algorithm for correlating the expression level or expression profile with disease state or outcome. In some embodiments, the kit further comprises a computer model or algorithm for designating a treatment modality for the individual. In some embodiments, the kit further comprises a computer model or algorithm for normalizing expression level or expression profile of the target sequences. In some embodiments, the kit further comprises a computer model or algorithm comprising a robust multichip average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based, nonlinear normalization, or a combination thereof.

Further disclosed herein is a kit for analyzing a cancer, comprising (a) a probe set comprising a plurality of target sequences, wherein the plurality of target sequences hybridizes to one or more targets selected from Table 6; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target sequences in a sample. In some embodiments, the kit further comprises a computer model or algorithm for correlating the expression level or expression profile with disease state or outcome. In some embodiments, the kit further comprises a computer model or algorithm for designating a treatment modality for the individual. In some embodiments, the kit further comprises a computer model or algorithm for normalizing expression level or expression profile of the target sequences. In some embodiments, the kit further comprises a computer model or algorithm comprising a robust multichip average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based, nonlinear normalization, or a combination thereof.

Disclosed herein, in some embodiments, is a classifier for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The classifier may comprise a classifier as disclosed in Table 17. The classifier can comprise a classifier as disclosed in Table 19. The classifier can comprise the GLM2, KNN12, KNN16, NB20, SVM5, SVM11, SVM20 classifiers or any combination thereof. The classifier can comprise a GLM2 classifier. Alternatively, the classifier comprises a KNN12 classifier. The classifier can comprise a KNN16 classifier. In other instances, the classifier comprises a NB20 classifier. The classifier may comprise a SVM5 classifier. In some instances, the classifier comprises a SVM11 classifier. Alternatively, the classifier comprises a SVM20 classifier. Alternatively, the classifier comprises one or more Inter-Correlated Expression (ICE) blocks disclosed herein. The classifier can comprise one or more probe sets disclosed herein. In some instances, the classifiers disclosed herein have an AUC value of at least about 0.50. In other instances, the classifiers disclosed herein have an AUC value of at least about 0.60. The classifiers disclosed herein can have an AUC value of at least about 0.70.

Further disclosed herein, is an Inter-Correlated Expression (ICE) block for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The ICE block may comprise one or more ICE Block IDs as disclosed in Tables 22-24. The ICE block can comprise Block ID_2879, Block ID_2922, Block ID_4271, Block ID_4627, Block ID_5080, or any combination thereof. Alternatively, the ICE block comprises Block ID_6592, Block ID_4226, Block ID_6930, Block ID_7113, Block ID_5470, or any combination thereof. In other instances, the ICE block comprises Block ID_7716, Block ID_4271, Block ID_5000, Block ID_5986, Block ID_1146, Block ID_7640, Block ID_4308, Block ID_1532, Block ID_2922, or any combination thereof. The ICE block can comprise Block ID_2922. Alternatively, the ICE block comprises Block ID_5080. In other instances, the ICE block comprises Block ID_6592. The ICE block can comprise Block ID_4627. Alternatively, the ICE block comprises Block ID_7113. In some instances, the ICE block comprises Block ID_5470. In other instances, the ICE block comprises Block ID_5155. The ICE block can comprise Block ID_6371. Alternatively, the ICE block comprises Block ID_2879.

Further disclosed herein, is a probe set for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The probe set may comprise a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% specificity. In some embodiments, the probe set further comprises a probe capable of detecting an expression level of at least one coding target.

Further disclosed herein, is a probe set for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The probe set may comprise a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% accuracy. In some embodiments, the probe set further comprises a probe capable of detecting an expression level of at least one coding target.

Further disclosed herein, is a probe selection region (PSR) for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The PSR can comprise any of the probe sets disclosed herein. Alternatively, the PSR comprises any of the probe sets as disclosed in Tables 4, 15, 17, 19, 22-24, and 27-30 (see ‘Probe set ID’ column). In some instances, the probe set comprises probe set ID 2518027. Alternatively, the probe set comprises probe set ID 3046448; 3046449; 3046450; 3046457; 3046459; 3046460; 3046461; 3046462; 3046465; 3956596; 3956601; 3956603; 3103704; 3103705; 3103706; 3103707; 3103708; 3103710; 3103712; 3103713; 3103714; 3103715; 3103717; 3103718; 3103720; 3103721; 3103725; 3103726; 2719689; 2719692; 2719694; 2719695; 2719696; 2642733; 2642735; 2642738; 2642739; 2642740; 2642741; 2642744; 2642745; 2642746; 2642747; 2642748; 2642750; 2642753; 3970026; 3970034; 3970036; 3970039; 2608321; 2608324; 2608326; 2608331; 2608332; 2536222; 2536226; 2536228; 2536229; 2536231; 2536232; 2536233; 2536234; 2536235; 2536236; 2536237; 2536238; 2536240; 2536241; 2536243; 2536245; 2536248; 2536249; 2536252; 2536253; 2536256; 2536260; 2536261; 2536262; 3670638; 3670639; 3670641; 3670644; 3670645; 3670650; 3670659; 3670660; 3670661; 3670666, a complement thereof, a reverse complement thereof, or any combination thereof.

Further disclosed herein in some embodiments is a system for analyzing a cancer, comprising: (a) a probe set comprising a plurality of target sequences, wherein (i) the plurality of target sequences hybridizes to one or more targets selected from Table 6; or (ii) the plurality of target sequences comprises one or more target sequences selected SEQ ID NOs: 1-903; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target hybridized to the probe in a sample from a subject suffering from a cancer.

In some instances, the plurality of targets disclosed herein comprises at least 5 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 10 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 15 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 20 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 30 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 35 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 40 targets selected from Table 6.

In some instances, the systems disclosed herein further comprise an electronic memory for capturing and storing an expression profile. The systems disclosed herein can further comprise a computer-processing device, optionally connected to a computer network. Alternatively, or additionally, the systems disclosed herein further comprise a software module executed by the computer-processing device to analyze an expression profile. In some instances, the systems disclosed herein further comprise a software module executed by the computer-processing device to compare the expression profile to a standard or control. The systems disclosed herein can further comprise a software module executed by the computer-processing device to determine the expression level of the target. The systems disclosed herein can further comprise a machine to isolate the target or the probe from the sample. In some instances systems disclosed herein further comprises a machine to sequence the target or the probe. Alternatively, or additionally, the systems disclosed herein further comprise a machine to amplify the target or the probe. The systems disclosed herein can further comprise a label that specifically binds to the target, the probe, or a combination thereof. In some embodiments, the systems disclosed herein further comprise a software module executed by the computer-processing device to transmit an analysis of the expression profile to the individual or a medical professional treating the individual. In some embodiments, the systems disclosed herein further comprise a software module executed by the computer-processing device to transmit a diagnosis or prognosis to the individual or a medical professional treating the individual. In some instances, the systems disclosed herein further comprise a sequencer for sequencing the plurality of targets. In other instances, the systems disclosed herein further comprise an instrument for amplifying the plurality of targets. In some embodiments, the systems disclosed herein further comprise a label for labeling the plurality of targets.

In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some instances, the cancer is a bladder cancer.

In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence.

The non-coding target can be selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some embodiments, the non-coding target is selected from an intronic sequence, a sequence within the UTR, or a non-coding RNA transcript. In some embodiments, the non-coding target is an intronic sequence or partially overlaps with an intronic sequence. In some embodiments, the non-coding target is a UTR sequence or partially overlaps with a UTR sequence.

In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the non-coding RNA transcript is non-polyadenylated.

In some instances, the coding target is selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence.

In some instances, the plurality of targets comprises at least about 2 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. Alternatively, or additionally, the plurality of targets comprises at least about 3 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 25 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the plurality of targets comprises at least about 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, or 425 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In other instances, the plurality of targets comprises at least about 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, or 900 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entireties to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Venn Diagram of the distribution of coding (a), non-coding (b) and non-exonic (c) PSRs found differentially expressed in normal versus primary tumor tissue (N vs P), primary versus metastatic Tissue (P vs M), and normal versus metastatic tissue (N vs M), respectively.

FIG. 2. Annotation of non-exonic PSRs and distribution of non-coding transcripts found to be differentially expressed between normal and primary tumour (a, d), primary tumour and metastatic tissue (b,e) and normal versus metastatic tissue (c,f). Those PSRs in the NC TRANSCRIPT slice of each pie chart are assessed for their overlap with non-coding transcripts to generate the categorization shown at the right for each pairwise comparison. AS: Antisense.

FIG. 3. MDS plots of the distribution of primary tumour samples with (circle) and without (square) metastatic events compared to metastatic (triangle) and normal (+) tissues for coding (a), non-coding (b) and non-exonic (c) probe sets.

FIG. 4. Kaplan-Meier plots of the two groups of primary tumor samples classified by KNN (more ‘normal-like’ vs. ‘metastatic-like’) using the biochemical recurrence (BCR) end point for coding (a), non-coding (b) and non-exonic (c).

FIG. 5. MDS plots of the distribution of primary tumour samples with Gleason score of 6 (circle), 7 (triangle), 8 and 9 (square) compared to metastatic (+) and normal (x) tissues for coding (a), non-coding (b) and non-exonic (c) PSRs.

FIG. 6. Illustration of (a) protein-coding and (b) non protein-coding gene structures.

FIG. 7. Illustration of the categorization of probe selection regions.

FIG. 8. List of potential probe selection regions.

FIG. 9. BCR KMM plot in MSKCC for different KNN models based on PSR genomic subsets

FIG. 10. Illustration of syntenic blocks.

FIG. 11. Venn Diagram distribution of differentially expressed transcripts across pairwise comparison. N vs P: Normal Adjacent versus Primary tumor comparison. P vs M: Primary Tumor versus Metastatic sample comparison. N vs M: Normal adjacent versus Metastatic Sample comparison.

FIG. 12. Heat map of genes with two or more transcripts differentially expressed across any pairwise comparison. Transcript names are provided as annotated in Ensembl. Heatmap is colored according to median expression values for Normal (N), Primary (P) and metastatic (M) samples. ‘*’ indicates that the transcript is protein-coding. Background indicates the expression value considered as background level based on control probe sets on the HuEx array.

FIG. 13. Heat map of genes with one or more transcripts differentially expressed across any pairwise comparison for which all transcripts were assessed. Transcript names are provided as annotated in Ensembl. Gene names are annotated based on their gene symbol. Heatmap is colored according to median expression values for Normal (N), Primary (P) and metastatic (M) samples. ‘*’ indicates that the transcript is protein-coding. ‘+’ indicates significant differential expression of a given transcript or gene. Background indicates the expression value considered as background level based on control probe sets on the HuEx array.

FIG. 14. Kaplan Meier plots of the two groups of primary tumor samples classified by KNN (“normal-like” vs “metastatic-like”) using the BCR endpoint for (a) Transcripts (represented by transcript-specific PSRs), (b) Kaftan nomogram and (c) Genes.

FIG. 15. Illustration of filtered and kept TS-PSRs. A) TS-PSR of a gene having only one transcript annotated. B) TS-PSRs for only one transcript of a gene with two or more transcripts. c) A gene for which at least two of its transcripts has a TS-PSR.

FIG. 16. Genomic Annotation and Distribution of the PSRs found differentially expressed within chr2q31.3 region.

FIG. 17. KM curve for a PSR (Probe set ID 2518027) for the BCR endpoint. P-value=0.00.

FIG. 18. Distribution of PSRs differentially expressed between low risk (GS<7) and high risk (GS>7) samples.

FIG. 19. (a) Box plots showing DIGS-RF12 segregating the Gleason 3+4 samples from the Gleason 4+3 samples. (b) KM plot of BCR-Free survival based on the groups predicted by DIGS-RF12.

FIG. 20. Genes with transcript-specific PSRs differentially expressed based on MSKCC data. (a) Gene CHRAC1. (b) Gene IMPDH1

FIG. 21. Depicts the ROC curves at 4 years (a) Survival ROC curves at 4 years for the training set for GC and GCC for patients with progression. (b) Survival ROC curves at 4 years for the testing set for GC and GCC for patients with progression.

FIG. 22. Discrimination Box plots for GC and GCC. Box plots depict the distribution of classifier scores between patients with and without progression. Boxes extend between the 25th and 75th percentiles (lower and upper quartiles, respectively), and the notch represents the 50th percentile (median). Whiskers extend indicating 95% confidence intervals.

FIG. 23. Calibration plots for GC and GCC. Calibration plots segregate the classifier scores into quintiles. For each quintile, mean score is plotted against the total proportion of patients who experienced progression. Perfect calibration, represented by the dashed 45-degree line, implies that the mean score is roughly equivalent to the proportion of patients who experienced progression (e.g. if the mean score is 0.20, then approximately 20% of patients in that quintile group experienced progression). Triangles represent the grouped patients, plotted by mean classifier score of that group against the observed frequency of progression. Compared to a poor model, a classifier that is a good discriminator will have a greater distance between the groups. The 95% confidence intervals are plotted for each group. Intercept indicates whether the predictions are systemically too high or too low, and an optimal slope approximately equals 1; slopes <1 indicate overfitting of the classifier.

FIG. 24. Cumulative incidence of disease progression for GC and GCC. Cumulative incidence curves were constructed using competing risks analysis to accommodate censoring due to death and other events that bias Kaplan-Meier estimates of incidence.

FIG. 25. Illustration of probe selection methods

FIG. 26. ROC curves (A) and KM plots (B) for NB20. (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 27. ROC curves (A) and KM plots (B) for KNN12. (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 28. ROC curves (A) and KM plots (B) for GLM2. (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 29. ROC curves (A) and KM plots (B) for a PSR intronic to gene MECOM (probe set ID 2704702). (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 30. ROC curves (A) and box plots (B) for SVM20. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 31. ROC curves (A) and box plots (B) for SVM11. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 32. ROC curves (A) and box plots (B) for SVM5. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 33. ROC curves (A) and box plots (B) for GLM2. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 34. Box plot (A) and ROC curve (B) for ICE Block 7716 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 35. Box plot (A) and ROC curve (B) for ICE Block 4271 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 36. Box plot (A) and ROC curve (B) for ICE Block 5000 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 37. Box plot (A) and ROC curve (B) for ICE Block 2922 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 38. Box plot (A) and ROC curve (B) for ICE Block 5080 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 39. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 6592 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 40. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 4627 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 41. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 7113 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 42. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 5470 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 43. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 5155 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 44. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 6371 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 45. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 2879 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 46. Discrimination of KNN16 in MSKCC upgrading testing set.

FIG. 47. ROC plot of clinical and pathological factors in comparison to KNN16.

FIG. 48. Heatmap of the 98 selected features in the pooled training and testing set.

FIG. 49. Multidimensional scaling of normal and tumor samples for lung and colorectal cancer. (A) MDS plots of normal (triangle) and cancer (circle) matched lung samples using differentially expressed non-coding RNA features. (B) MDS plots of normal (triangle) and cancer (circle) colorectal samples using differentially expressed non-coding RNA features.

FIG. 50. Multidimensional scaling and expression density curve of tumor samples at different progression stages for lung and colorectal cancer. (A) MDS plots of tumor stage I (triangle) and stages II and III (circle) lung samples using differentially expressed non-coding RNA features. (B) Expression density of the XIST-associated PSR 4012540 for stage II (dotted line) and stage III (solid line) colorectal carcinomas.

Table 1. List of Abbreviations.

Table 2. Summary of the clinical characteristics of the dataset used in Example 1.

Table 3. Definitions of Ensembl ‘Transcript Biotype’ annotations for non-coding transcripts found differentially expressed.

Table 4. Long non-coding RNAs differentially expressed in prostate cancer.

Table 5. Logistic regression analysis for prediction of the probability of clinical recurrence (CR). SVI: Seminal Vesicle Invasion; ECE: Extracapsular Extension; SMS: Surgical Margin Status; LNI: Lymph node Involvement; PreTxPSA: Pre-operative PSA; PGS: Pathological Gleason Score.

Table 6. List of Coding probe selection regions (coding PSRs) and Non-coding probe selection regions (non-coding PSRs).

Table 7. Protein-coding genes with non-coding transcripts differentially expressed. NvsP: Normal Adjacent versus Primary tumor comparison. PvsM: Primary Tumor versus Metastatic sample comparison. NvsM: Normal adjacent versus Metastatic Sample comparison.

Table 8. Transcripts found differentially expressed across all pairwise comparison (top) and across Normal vs Primary Tumor and Primary Tumor vs Metastatic samples comparisons (bottom). (*) indicates upregulation. No (*) indicates downregulation. N.A.: Not Applicable.

Table 9. Multivariable Logistic Regression Analysis of transcripts (represented by Transcript-Specific PSRs) and genes adjusted by Kattan Nomogram. KNN-positive: metastatic-like. *: Greater than 50% probability of BCR used as cut-off OR: Odds Ratio. CI: Confidence Interval.

Table 10. Characteristics of the study population.

Table 11. Multivariable Cox proportional hazards modeling of clinicopathologic features.

Table 12. Classifier performance of clinicopathologic features. In addition, two multivariate clinical classifiers were built using a logistic model (CC1) as well as a Cox model (CC2).

Table 13. Multivariable Cox proportional hazards modeling of GC and clinicopathologic features.

Table 14. Raw clinical data, QC results, training and testing sets and classifier scores for each of the 251 samples.

Table 15. List of probe sets and associated genes that overlap with KNN89 PSRs.

Table 16. Machine Learning algorithms, ranking, standardization methods and number of features included in each classifier. Additionally, the performance based on AUC is included for the training and testing sets.

Table 17. Sequences composing the classifiers. For each sequence, the chromosomal coordinates, associated gene (if not intergenic), type of feature (coding or non-coding), and classifier(s) are listed.

Table 18. Machine Learning algorithms, ranking, standardization methods and number of features included in each classifier. Additionally, the performance based on AUC is included for the training and testing sets.

Table 19. Sequences composing the classifiers. For each sequence, the chromosomal coordinates, associated gene (if not intergenic), type of feature (coding or non-coding), and classifier(s) are listed.

Table 20. Number of ICE blocks found across different comparisons and different correlation thresholds. Numbers in parenthesis indicate the number of ICE blocks found differentially expressed when using a P-value threshold of 0.05.

Table 21. Number of ICE blocks differentially expressed across different compositions of coding and non-coding PSRs, different correlation thresholds and different comparisons. The number of ICE blocks found differentially expressed is obtained by using a P-value threshold of 0.05.

Table 22. ICE blocks found differentially expressed for the Gleason Score comparison when using a strict correlation threshold of 0.9. For each ICE block, the following information is provided: Block ID, Wilcoxon P-value, chromosomal location, number of overlapping genes across the genomic span of the ICE block, overlapping genes, Composition of the ICE block as a percentage of coding and non-coding PSRs, number of PSRs composing the ICE block and Probe set IDs that correspond to the PSRs composing the ICE block.

Table 23. ICE blocks found differentially expressed for the Biochemical Recurrence comparison when using a strict correlation threshold of 0.9. For each ICE block, the following information is provided: Block ID, Wilcoxon P-value, chromosomal location, number of overlapping genes across the genomic span of the ICE block, overlapping genes, Composition of the ICE block as a percentage of coding and non-coding PSRs, number of PSRs composing the ICE block and Probe set IDs that correspond to the PSRs composing the ICE block.

Table 24. Sequences and Probe set IDs associated to the PSRs composing the ICE blocks assessed in FIGS. 33-44.

Table 25. The number of cases and controls in the training and testing set.

Table 26. Features used for modeling a KNN classifier.

Table 27. Differentially expressed non-coding RNA features between normal and tumor lung cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

Table 28. Differentially expressed non-coding RNA features between normal and tumor colorectal cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

Table 29. Differentially expressed non-coding RNA features between stage I and stage II+III lung cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

Table 30. Differentially expressed non-coding RNA features between stage II and stage III colorectal cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

DETAILED DESCRIPTION OF THE INVENTION

The present invention discloses systems and methods for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject using expression-based analysis of coding targets, non-coding targets, and/or non-exonic transcripts. Generally, the method comprises (a) optionally providing a sample from a subject suffering from a cancer; (b) assaying the expression level for a plurality of targets in the sample; and (c) diagnosing, predicting and/or monitoring the status or outcome of the cancer based on the expression level of the plurality of targets.

Assaying the expression level for a plurality of targets in the sample may comprise applying the sample to a microarray. In some instances, assaying the expression level may comprise the use of an algorithm. The algorithm may be used to produce a classifier. Alternatively, the classifier may comprise a probe selection region. Assaying the expression level for a plurality of targets may comprise detecting and/or quantifying the plurality of targets.

In some instances, the plurality of targets may comprise a coding target and a non-coding target and the non-coding target is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. Alternatively, the plurality of targets may comprise a coding target and a non-coding target, wherein the non-coding target does not comprise a miRNA, an intronic sequence, and a UTR sequence. In other instances, the plurality of targets may consist essentially of a non-coding target selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, wherein the non-coding RNA transcript comprises a piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, or LSINCTs. The plurality of targets may also comprise a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated.

In some instances, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 1-903. In other instances, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target comprises a sequence selected from SEQ ID NOs.: 262-291.

Further disclosed herein, is a probe set for diagnosing, predicting, and/or monitoring a cancer in a subject. In some instances, the probe set comprises a plurality of probes capable of detecting an expression level of at least one non-coding RNA transcript, wherein the expression level determines the cancer status or outcome of the subject with at least about 45% specificity. In some instances, the probe set comprises a plurality of probes capable of detecting an expression level of at least one non-coding RNA transcript, wherein the expression level determines the cancer status or outcome of the subject with at least about 45% accuracy.

Further disclosed herein are methods for characterizing a patient population. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying the expression level for a plurality of targets in the sample; and (c) characterizing the subject based on the expression level of the plurality of targets. In some instances, the plurality of targets comprises one or more coding targets and one or more non-coding targets. In some instances, the coding target comprises an exonic region or a fragment thereof. The non-coding targets can comprise a non-exonic region or a fragment thereof. Alternatively, the non-coding target may comprise the UTR of an exonic region or a fragment thereof.

In some instances, characterizing the subject comprises determining whether the subject would respond to an anti-cancer therapy. Alternatively, characterizing the subject comprises identifying the subject as a non-responder to an anti-cancer therapy. Optionally, characterizing the subject comprises identifying the subject as a responder to an anti-cancer therapy.

Before the present invention is described in further detail, it is to be understood that this invention is not limited to the particular methodology, compositions, articles or machines described, as such methods, compositions, articles or machines can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.

Definitions

Unless defined otherwise or the context clearly dictates otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In describing the present invention, the following terms may be employed, and are intended to be defined as indicated below.

The term “polynucleotide” as used herein refers to a polymer of greater than one nucleotide in length of ribonucleic acid (RNA), deoxyribonucleic acid (DNA), hybrid RNA/DNA, modified RNA or DNA, or RNA or DNA mimetics, including peptide nucleic acids (PNAs). The polynucleotides may be single- or double-stranded. The term includes polynucleotides composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotides having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotides are well known in the art and for the purposes of the present invention, are referred to as “analogues.”

“Complementary” or “substantially complementary” refers to the ability to hybridize or base pair between nucleotides or nucleic acids, such as, for instance, between a sensor peptide nucleic acid or polynucleotide and a target polynucleotide. Complementary nucleotides are, generally, A and T (or A and U), or C and G. Two single-stranded polynucleotides or PNAs are said to be substantially complementary when the bases of one strand, optimally aligned and compared and with appropriate insertions or deletions, pair with at least about 80% of the bases of the other strand, usually at least about 90% to 95%, and more preferably from about 98 to 100%.

Alternatively, substantial complementarity exists when a polynucleotide may hybridize under selective hybridization conditions to its complement. Typically, selective hybridization may occur when there is at least about 65% complementarity over a stretch of at least 14 to 25 bases, for example at least about 75%, or at least about 90% complementarity. See, M. Kanehisa, Nucleic Acids Res. 12:203 (1984).

“Preferential binding” or “preferential hybridization” refers to the increased propensity of one polynucleotide to bind to its complement in a sample as compared to a noncomplementary polymer in the sample.

Hybridization conditions may typically include salt concentrations of less than about 1M, more usually less than about 500 mM, for example less than about 200 mM. In the case of hybridization between a peptide nucleic acid and a polynucleotide, the hybridization can be done in solutions containing little or no salt. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., and more typically greater than about 30° C., for example in excess of about 37° C. Longer fragments may require higher hybridization temperatures for specific hybridization as is known in the art. Other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, presence of organic solvents and extent of base mismatching, and the combination of parameters used is more important than the absolute measure of any one alone. Other hybridization conditions which may be controlled include buffer type and concentration, solution pH, presence and concentration of blocking reagents to decrease background binding such as repeat sequences or blocking protein solutions, detergent type(s) and concentrations, molecules such as polymers which increase the relative concentration of the polynucleotides, metal ion(s) and their concentration(s), chelator(s) and their concentrations, and other conditions known in the art.

“Multiplexing” herein refers to an assay or other analytical method in which multiple analytes can be assayed simultaneously.

A “target sequence” as used herein (also occasionally referred to as a “PSR” or “probe selection region”) refers to a region of the genome against which one or more probes can be designed. Exemplary probe selection regions are depicted in FIGS. 7-8. A “target sequence” may be a coding target or a non-coding target. A “target sequence” may comprise exonic and/or non-exonic sequences. Alternatively, a “target sequence” may comprise an ultraconserved region. An ultraconserved region is generally a sequence that is at least 200 base pairs and is conserved across multiple species. An ultraconserved region may be exonic or non-exonic. Exonic sequences may comprise regions on a protein-coding gene, such as an exon, UTR, or a portion thereof. Non-exonic sequences may comprise regions on a protein-coding, non protein-coding gene, or a portion thereof. For example, non-exonic sequences may comprise intronic regions, promoter regions, intergenic regions, a non-coding transcript, an exon anti-sense region, an intronic anti-sense region, UTR anti-sense region, non-coding transcript anti-sense region, or a portion thereof.

As used herein, a probe is any polynucleotide capable of selectively hybridizing to a target sequence, a complement thereof, a reverse complement thereof, or to an RNA version of the target sequence, the complement thereof, or the reverse complement thereof. A probe may comprise ribonucleotides, deoxyribonucleotides, peptide nucleic acids, and combinations thereof. A probe may optionally comprise one or more labels. In some embodiments, a probe may be used to amplify one or both strands of a target sequence or an RNA form thereof, acting as a sole primer in an amplification reaction or as a member of a set of primers.

As used herein, the term “probe set” refers to a set of synthetic oligonucleotide probes. The oligonucleotide probes can be on Exon arrays that interrogate gene expression from one exon. Often, the probe set comprises four probes. Probes of the probe set can anneal to the sense strand of a coding transcript and/or a non-coding transcript. In some instances, the probes of the probe set are located on an array. The probes of the probe set can be located on the array in an antisense orientation. In some instances, a probe set can refer to a probe set as described by Affymetrix (at the world wide web at microarrays.ca/services/exonarray_design_technote.pdf).

As used herein, the term “probe selection region” (“PSR”) is often the smallest unit on an array for expression profiling. In some instances, a PSR is represented by an individual probe set. The PSR can be an exon or overlap with an exon. The PSR can comprise or overlap with at least a portion of a coding transcript. Alternatively, a PSR can comprise or overlap with at least a portion of a non-coding transcript. In some instances, an exon cluster (e.g., a group of overlapping exons) can be divided into multiple PSRs. In some instances, a probe set can refer to a PSR as described by Affymetrix (at the world wide web at microarrays.ca/services/exonarray_design_technote.pdf). In some instances, the terms “PSR”, “probe selection region”, and “probe set” can be used interchangeably to refer to a region on a coding transcript and/or non-coding transcript. In some instances, the region represented by the probe set comprises a sequence that is antisense to the PSR.

In some instances, the probe sets and PSRs can be used to interrogate expression from coding transcripts and/or non-coding transcripts. Probe set IDs as disclosed in Tables 17, 19, 22-24, and 27-30 refer to probe sets as described by Affymetrix (at the world wide web at affymetrix.com/analysis/index.affx).

As used herein, a non-coding target may comprise a nucleotide sequence. The nucleotide sequence is a DNA or RNA sequence. A non-coding target may include a UTR sequence, an intronic sequence, or a non-coding RNA transcript. A non-coding target also includes sequences which partially overlap with a UTR sequence or an intronic sequence. A non-coding target also includes non-exonic transcripts.

As used herein, a non-coding RNA (ncRNA) transcript is an RNA transcript that does not encode a protein. ncRNAs include short ncRNAs and long ncRNAs (lncRNAs). Short ncRNAs are ncRNAs that are generally 18-200 nucleotides (nt) in length. Examples of short ncRNAs include, but are not limited to, microRNAs (miRNAs), piwi-associated RNAs (piRNAs), short interfering RNAs (siRNAs), promoter-associated short RNAs (PASRs), transcription initiation RNAs (tiRNAs), termini-associated short RNAs (TASRs), antisense termini associated short RNAs (aTASRs), small nucleolar RNAs (snoRNAs), transcription start site antisense RNAs (TSSa-RNAs), small nuclear RNAs (snRNAs), retroposon-derived RNAs (RE-RNAs), 3′UTR-derived RNAs (uaRNAs), x-ncRNA, human Y RNA (hY RNA), unusually small RNAs (usRNAs), small NF90-associated RNAs (snaRs), vault RNAs (vtRNAs), small Cajal body-specific RNAs (scaRNAs), and telomere specific small RNAs (tel-sRNAs). LncRNAs are cellular RNAs, exclusive of rRNAs, greater than 200 nucleotides in length and having no obvious protein-coding capacity (Lipovich L, et al., MacroRNA underdogs in a microRNA world: evolutionary, regulatory, and biomedical significance of mammalian long non-protein-coding RNA, Biochim Biophys Acta, 2010, 1799(9): 597-615). LncRNAs include, but are not limited to, large or long intergenic ncRNAs (lincRNAs), transcribed ultraconserved regions (T-UCRs), pseudogenes, GAA-repeat containing RNAs (GRC-RNAs), long intronic ncRNAs, antisense RNAs (aRNAs), promoter-associated long RNAs (PALRs), promoter upstream transcripts (PROMPTs), and long stress-induced non-coding transcripts (LSINCTs).

As used herein, a coding target includes nucleotide sequences that encode for a protein and peptide sequences. The nucleotide sequence is a DNA or RNA sequence. The coding target includes protein-coding sequence. Protein-coding sequences include exon-coding sequences (e.g., exonic sequences).

As used herein, diagnosis of cancer may include the identification of cancer in a subject, determining the malignancy of the cancer, or determining the stage of the cancer.

As used herein, prognosis of cancer may include predicting the clinical outcome of the patient, assessing the risk of cancer recurrence, determining treatment modality, or determining treatment efficacy.

“Having” is an open-ended phrase like “comprising” and “including,” and includes circumstances where additional elements are included and circumstances where they are not.

“Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event or circumstance occurs and instances in which it does not.

As used herein, the term “metastasis” (“Mets”) describes the spread of a cancer from one part of the body to another. A tumor formed by cells that have spread can be called a “metastatic tumor” or a “metastasis.” The metastatic tumor often contains cells that are like those in the original (primary) tumor.

As used herein, the term “progression” describes the course of a disease, such as a cancer, as it becomes worse or spreads in the body.

As used herein, the term “about” refers to approximately +/−10% variation from a given value. It is to be understood that such a variation is always included in any given value provided herein, whether or not it is specifically referred to.

Use of the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, reference to “a polynucleotide” includes a plurality of polynucleotides, reference to “a target” includes a plurality of such targets, reference to “a normalization method” includes a plurality of such methods, and the like. Additionally, use of specific plural references, such as “two,” “three,” etc., read on larger numbers of the same subject, unless the context clearly dictates otherwise.

Terms such as “connected,” “attached,” “linked” and “conjugated” are used interchangeably herein and encompass direct as well as indirect connection, attachment, linkage or conjugation unless the context clearly dictates otherwise.

Where a range of values is recited, it is to be understood that each intervening integer value, and each fraction thereof, between the recited upper and lower limits of that range is also specifically disclosed, along with each subrange between such values. The upper and lower limits of any range can independently be included in or excluded from the range, and each range where either, neither or both limits are included is also encompassed within the invention. Where a value being discussed has inherent limits, for example where a component can be present at a concentration of from 0 to 100%, or where the pH of an aqueous solution can range from 1 to 14, those inherent limits are specifically disclosed. Where a value is explicitly recited, it is to be understood that values, which are about the same quantity or amount as the recited value, are also within the scope of the invention, as are ranges based thereon. Where a combination is disclosed, each sub-combination of the elements of that combination is also specifically disclosed and is within the scope of the invention. Conversely, where different elements or groups of elements are disclosed, combinations thereof are also disclosed. Where any element of an invention is disclosed as having a plurality of alternatives, examples of that invention in which each alternative is excluded singly or in any combination with the other alternatives are also hereby disclosed; more than one element of an invention can have such exclusions, and all combinations of elements having such exclusions are hereby disclosed.

Coding and Non-Coding Targets

The methods disclosed herein often comprise assaying the expression level of a plurality of targets. The plurality of targets may comprise coding targets and/or non-coding targets of a protein-coding gene or a non protein-coding gene. As depicted in FIG. 6A, a protein-coding gene structure may comprise an exon and an intron. The exon may further comprise a coding sequence (CDS) and an untranslated region (UTR). The protein-coding gene may be transcribed to produce a pre-mRNA and the pre-mRNA may be processed to produce a mature mRNA. The mature mRNA may be translated to produce a protein.

As depicted in FIG. 6B, a non protein-coding gene structure may comprise an exon and intron. Usually, the exon region of a non protein-coding gene primarily contains a UTR. The non protein-coding gene may be transcribed to produce a pre-mRNA and the pre-mRNA may be processed to produce a non-coding RNA (ncRNA).

FIG. 7 illustrates potential targets (e.g., probe selection regions) within a protein-coding gene and a non protein-coding gene. A coding target may comprise a coding sequence of an exon. A non-coding target may comprise a UTR sequence of an exon, intron sequence, intergenic sequence, promoter sequence, non-coding transcript, CDS antisense, intronic antisense, UTR antisense, or non-coding transcript antisense. A non-coding transcript may comprise a non-coding RNA (ncRNA).

In some instances, the plurality of targets may be differentially expressed. For example, as shown in FIG. 20A, the CHRAC1-001 transcript specific probe selection region (probe set ID 3118459), the CHRAC1-003 transcript specific probe selection region (probe set ID 3118456) and the CHRAC1-005 transcript specific p probe selection region (probe set ID 3118454) demonstrate that the CHRAC1-001, -003, and -005 transcripts are differentially expressed in the Primary vs Normal and the Primary vs Mets. FIG. 20B provides another example of the differential expression of gene with transcript-specific PSRs.

In some instances, adjacent and differentially expressed PSRs can form a block of differentially expressed PSRs (e.g., syntenic block). For example, as shown in FIG. 10B, a plurality of differentially expressed and adjacent PSRs (based on the bars of the transcriptional profile) may form one syntenic block (as depicted by the rectangle). A syntenic block may comprise one or more genes. The syntenic block as depicted in FIG. 10B corresponds to the three genes, RP11-39404.2, MIR143, MIR145 depicted in FIG. 10A. In some instances, the syntenic block may comprise PSRs specific to a coding target, non-coding targets, or a combination thereof. In some instances, as shown in FIG. 10A-B, the syntenic block comprises PSRs specific to a non-coding target. In some instances, the syntenic blocks may be categorized according to their components. For example, the syntenic block depicted in FIG. 10B would be a non-coding syntenic block differentially expressed which is composed of non-coding targets such as miRNAs, intergenic regions, etc.

In some instances, a plurality of PSRs is differentially expressed. The differentially expressed PSRs may form one or more syntenic blocks. As shown in FIG. 10C, differentially expressed PSRs may form two or more syntenic blocks (as outlined by the boxes). In some instances, the two or more syntenic blocks may correspond to one or more molecules. For example, two or more syntenic blocks could correspond to a non-coding target. Alternatively, two or more syntenic blocks may correspond to a coding target.

In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the non-coding target is located on chr2q31.3. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 262-291. In some instances, the non-coding target is a lncRNA. The lncRNA can be a vlncRNA or vlincRNA.

In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the non-coding target comprises a sequence that is complementary to a sequence located on chr2q31.3. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the coding target is located on chr2q31.3. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the coding target comprises a sequence that is complementary to a sequence located on chr2q31.3. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the plurality of targets comprises a coding target and/or a non-coding target. The plurality of targets can comprise any of the coding targets and/or non-coding targets disclosed herein. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target is located on chr2q31.3. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to a sequence located on chr2q31.3. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 262-291.

Alternatively, a non-coding target comprises a UTR sequence, an intronic sequence, or a non-coding RNA transcript. In some instances, a non-coding target comprises sequences which partially overlap with a UTR sequence or an intronic sequence. A non-coding target also includes non-exonic transcripts. Exonic sequences may comprise regions on a protein-coding gene, such as an exon, UTR, or a portion thereof. Non-exonic sequences may comprise regions on a protein-coding, non protein-coding gene, or a portion thereof. For example, non-exonic sequences may comprise intronic regions, promoter regions, intergenic regions, a non-coding transcript, an exon anti-sense region, an intronic anti-sense region, UTR anti-sense region, non-coding transcript anti-sense region, or a portion thereof.

In some instances, the coding target and/or non-coding target is at least about 70% identical to a sequence selected from SEQ ID NOs.: 1-903. Alternatively, the coding target and/or non-coding target is at least about 80% identical to a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target and/or non-coding target is at least about 85% identical to a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target and/or non-coding target is at least about 90% identical to a sequence selected from SEQ ID NOs.: 1-903. Alternatively, the coding target and/or non-coding target are at least about 95% identical to a sequence selected from SEQ ID NOs.: 1-903.

In some instances, the plurality of targets comprises two or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises three or more sequences selected (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises five or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises six or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises ten or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises fifteen or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises twenty or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises twenty five or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises thirty or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof.

In some instances, the plurality of targets disclosed herein comprises a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 bases or base pairs in length. In other instances, the plurality of targets disclosed herein comprises a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 kilo bases or kilo base pairs in length. Alternatively, the plurality of targets disclosed herein comprises a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 mega bases or mega base pairs in length. The plurality of targets disclosed herein can comprise a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 giga bases or giga base pairs in length.

In some instances, the non-coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 bases or base pairs in length. In other instances, the non-coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 kilo bases or kilo base pairs in length. Alternatively, the non-coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 mega bases or mega base pairs in length. The non-coding target can be at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 giga bases or giga base pairs in length.

In some instances, the coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 bases or base pairs in length. In other instances, the coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 kilo bases or kilo base pairs in length. Alternatively, the coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 mega bases or mega base pairs in length. The coding target can be at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 giga bases or giga base pairs in length.

Non-Coding RNAs

In some instances, the plurality of targets comprises a non-coding RNA. Generally, non-coding RNAs (ncRNAs) are functional transcripts that do not code for proteins. ncRNAs are loosely grouped into two major classes based on transcript size: small ncRNAs and large ncRNAs (lncRNAs).

Small ncRNAs

Small ncRNAs are typically 18 to 200 nucleotides (nt) in size and may be processed from longer precursors. Examples of small ncRNAs include, but are not limited to, microRNAs (miRNAs), piwi-associated RNAs (piRNAs), short interfering RNAs (siRNAs), promoter-associated short RNAs (PASRs), transcription initiation RNAs (tiRNAs), termini-associated short RNAs (TASRs), antisense termini associated short RNAs (aTASRs), small nucleolar RNAs (snoRNAs), transcription start site antisense RNAs (TSSa-RNAs), small nuclear RNAs (snRNAs), retroposon-derived RNAs (RE-RNAs), 3′UTR-derived RNAs (uaRNAs), x-ncRNA, human Y RNA (hY RNA), unusually small RNAs (usRNAs), small NF90-associated RNAs (snaRs), vault RNAs (vtRNAs), small Cajal body-specific RNAs (scaRNAs), and telomere specific small RNAs (tel-sRNAs).

miRNAs

miRNAs can be divided into two subclasses: canonical and non-canonical miRNAs. Canonical miRNAs may initially be transcribed as long RNAs that contain hairpins. The 60-75 nt hairpins can be recognized by the RNA-binding protein Dgcr8 (DiGeorge syndrome critical region 8), which may direct the RNase III enzyme Drosha to cleave the base of the hairpin. Following cleavage by the Drosha-Dgcr8 complex, also called the microprocessor, the released hairpin may be transported to the cytoplasm, where Dicer, another RNase III enzyme, then cleaves it into a single short 18-25 nt dsRNA. Non-canonical miRNAs may bypass processing by the microprocessor by using other endonucleases or by direct transcription of a short hairpin. The resulting pre-miRNAs can then be exported from the nucleus and cleaved once by Dicer.

piRNAs

The piRNAs may differ from the miRNAs and endo-siRNAs in that they often do not require Dicer for their processing. piRNAs may be 25-32 nt in length, and can be expressed in the germline in mammals. They may be defined by their interaction with the Piwi proteins, a distinct family of Argonaute proteins (including Miwi, Miwi2 and Mili in mouse; also known as Piwil1, Piwil4 and Piwil2, respectively). piRNAs can be generated from long single-stranded RNA precursors that are often encoded by complex and repetitive intergenic sequences.

siRNAs

siRNAs can be derived from long dsRNAs in the form of either sense or antisense RNA pairs or as long hairpins, which may then directly be processed by Dicer consecutively along the dsRNA to produce multiple siRNAs. Therefore, canonical miRNAs, non-canonical miRNAs and endo-siRNAs may involve Dicer processing and can be ˜21 nt in length. Furthermore, in all three cases, one strand of the Dicer product may associate with an Argonaute protein (Ago 1-4 in mammals; also known as Eif2c1-4) to form the active RISC (RNA-induced silencing complex). Often, these ribonucleoprotein complexes may be able to bind to and control the levels and translation of their target mRNAs, if the match between the small RNA and its target is perfect, the target is cleaved; if not, the mRNA is destabilized through as yet unresolved mechanisms.

PASRs, tiRNAs, and TSSa-RNAs

PASRs can be broadly defined as short transcripts, generally 20-200 nt long, capped, with 5′ ends that coincide with the transcription start sites (TSSs) of protein and non-coding genes. TiRNAs are predominantly 18 nt in length and generally found downstream of TSSs. TSSa-RNAs can be 20-90 nt long and may be localized within −250 to +50 base pairs of transcription start sites (TSSs). PASRs, tiRNAs, and TSSa-RNAs may strongly associate with highly expressed genes and regions of RNA Polymerase II (RNAPII) binding, may be weakly expressed, and may show bidirectional distributions that mirror RNAPII (Taft J, et al., Evolution, biogenesis and function of promoter-associated RNAs, Cell Cycle, 2009, 8(15):2332-2338).

TASRs and aTASRs

TASRs may be 22-200 nt in length and are found to cluster at 5′ and 3′ termini of annotated genes. aTASRs can be found within 50 bp and antisense to 3′ UTRs of annotated transcripts.

snoRNAs

SnoRNAs represent one of the largest groups of functionally diverse trans-acting ncRNAs currently known in mammalian cells. snoRNAs can range between 60-150 nucleotides in length. From a structural basis, snoRNAs may fall into two categories termed box C/D snoRNAs (SNORDs) and box H/ACA snoRNAs (SNORAs). SNORDs can serve as guides for the 2′-O-ribose methylation of rRNAs or snRNAs, whereas SNORAs may serve as guides for the isomerization of uridine residues into pseudouridine.

snRNAs

snRNAs, historically referred to as U-RNAs, may be less than 200 nt long and may play key roles in pre-mRNA splicing. snRNAs are further divided into two main categories based on shared sequences and associated proteins. Sm-class RNAs can have a 5′ trimethylguanosine cap and bind several Sm proteins. Lsm-RNAs may possess a monomethylphosphate 5′ cap and a uridine rich 3′ end acting as a binding site for Lsm proteins. Sm class of snRNAs (U1, U2, U4 and U5) are synthesized by RNA Pol II. For Sm class, pre-snRNAs are transcribed and 5′ monomethylguanosine capped in the nucleus, exported via multiple factors to the cytoplasm for further processing. After cytoplamic hypermethylation of 5′ cap (trimethylguanosine) and 3′ trimming, the snRNA is translocated back into the nucleus. snRNPs for Sm class snRNAs are also assembled in the cytosol. Lsm snRNA (U6 and other snoRNAs) are transcribed by Pol III and keep the monomethylguanosine 5′ cap and in the nucleus. Lsm snRNAs never leave the nucleus.

lncRNAs

LncRNAs are cellular RNAs, exclusive of rRNAs, greater than 200 nucleotides in length and having no obvious protein-coding capacity (Lipovich L, et al., MacroRNA underdogs in a microRNA world: evolutionary, regulatory, and biomedical significance of mammalian long non-protein-coding RNA, Biochim Biophys Acta, 2010, 1799(9):597-615). LncRNAs include, but are not limited to, large or long intergenic ncRNAs (lincRNAs), transcribed ultraconserved regions (T-UCRs), pseudogenes, GAA-repeat containing RNAs (GRC-RNAs), long intronic ncRNAs, antisense RNAs (aRNAs), promoter-associated long RNAs (PALRs), promoter upstream transcripts (PROMPTs), long stress-induced non-coding transcripts (LSINCTs), very long non-coding RNAs (vlncRNAs), and very long intergenic non-coding RNA (vlincRNAs). vlncRNAs (very long non-coding RNAs) are a type of lncRNAs that are often greater than 5 kb long and for which detailed information is available. vlincRNAs (very long intergenic non-coding RNAs) are generally expressed intergenic regions. In some instances, the vlincRNAs are at least about 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, or 100 kb in length (Kapranov P et al., 2010, BMC Biol, 8:149).

T-UCRs

T-UCRs are transcribed genomic elements longer than 200 base pairs (bp) (range: 200-779 bp) that are absolutely conserved (100% identity with no insertion or deletions) among mouse, rat, and human genomes. T-UCRs may be intergenic (located between genes), intronic, exonic, partially exonic, exon containing, or “multiple” (location varies because of gene splice variants).

Pseudogenes

Pseudogenes are commonly defined as sequences that resemble known genes but cannot produce functional proteins. Pseudogenes can be broadly classified into two categories: processed and nonprocessed. Nonprocessed pseudogenes usually contain introns, and they are often located next to their paralogous parent gene. Processed pseudogenes are thought to originate through retrotransposition; accordingly, they lack introns and a promoter region, but they often contain a polyadenylation signal and are flanked by direct repeats.

Probes/Primers

The present invention provides for a probe set for diagnosing, monitoring and/or predicting a status or outcome of a cancer in a subject comprising a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% specificity.

The probe set may comprise one or more polynucleotide probes. Individual polynucleotide probes comprise a nucleotide sequence derived from the nucleotide sequence of the target sequences, complementary sequences thereof, or reverse complement sequences thereof. The nucleotide sequence of the polynucleotide probe is designed such that it corresponds to, is complementary to, or is reverse complementary to the target sequences. The polynucleotide probe can specifically hybridize under either stringent or lowered stringency hybridization conditions to a region of the target sequences, to the complement thereof, or to a nucleic acid sequence (such as a cDNA, RNA) derived therefrom.

The selection of the polynucleotide probe sequences and determination of their uniqueness may be carried out in silico using techniques known in the art, for example, based on a BLASTN search of the polynucleotide sequence in question against gene sequence databases, such as the Human Genome Sequence, UniGene, dbEST or the non-redundant database at NCBI. In one embodiment of the invention, the polynucleotide probe is complementary to a region of a target mRNA derived from a target sequence in the probe set. Computer programs can also be employed to select probe sequences that may not cross hybridize or may not hybridize non-specifically.

FIG. 25 illustrates in an exemplary approach to selecting probes, also referred to herein as biomarkers, useful in diagnosing, predicting, and/or monitoring the status or outcome of a cancer, in accordance with an embodiment of this invention. In some instances, microarray hybridization of RNA, extracted from prostate cancer tissue samples and amplified, may yield a dataset that is then summarized and normalized by the fRMA technique (See McCall et al., “Frozen robust multiarray analysis (fRMA),” Biostatistics Oxford England 11.2 (2010): 242-253). The raw expression values captured by the probes can be summarized and normalized into PSR values. Cross-hybridizing probe sets, highly variable PSRs (e.g., PSRs with variance above the 90th percentile), and probe sets containing less than 4 probes can be removed or filtered. Following fRMA and filtration, the data can be decomposed into its principal components and an analysis of variance model can be used to determine the extent to which a batch effect remains present in the first 10 principal components (see Leek et al. “Tackling the widespread and critical impact of batch effects in high-throughput data,” Nat. Rev. Genetics 11.10 (2010): 733-739).

These remaining probe sets can be further refined by filtration by a T-test between CR (clinical recurrence) and non-CR samples. In some instances, the probe sets with a P-value of >0.01 can be removed or filtered. The remaining probe sets can undergo further selection. Feature selection can be performed by regularized logistic regression using the elastic-net penalty (see Zou & Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Stat. Soc.—Series B: Statistical Methodology 67.2 (2005): 301-320). The regularized regression can be bootstrapped over 1000 times using all training data. With each iteration of bootstrapping, probe sets that have non-zero co-efficient following 3-fold cross validation can be tabulated. In some instances, probe sets that were selected in at least 25% of the total runs can be used for model building.

One skilled in the art understands that the nucleotide sequence of the polynucleotide probe need not be identical to its target sequence in order to specifically hybridize thereto. The polynucleotide probes of the present invention, therefore, comprise a nucleotide sequence that is at least about 65% identical to a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 70% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 75% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 80% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 85% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 90% identical a region of the coding target or non-coding target. In a further embodiment, the nucleotide sequence of the polynucleotide probe is at least about 95% identical to a region of the coding target or non-coding target.

Methods of determining sequence identity are known in the art and can be determined, for example, by using the BLASTN program of the University of Wisconsin Computer Group (GCG) software or provided on the NCBI website. The nucleotide sequence of the polynucleotide probes of the present invention may exhibit variability by differing (e.g. by nucleotide substitution, including transition or transversion) at one, two, three, four or more nucleotides from the sequence of the coding target or non-coding target.

Other criteria known in the art may be employed in the design of the polynucleotide probes of the present invention. For example, the probes can be designed to have <50% G content and/or between about 25% and about 70% G+C content. Strategies to optimize probe hybridization to the target nucleic acid sequence can also be included in the process of probe selection.

Hybridization under particular pH, salt, and temperature conditions can be optimized by taking into account melting temperatures and by using empirical rules that correlate with desired hybridization behaviors. Computer models may be used for predicting the intensity and concentration-dependence of probe hybridization.

The polynucleotide probes of the present invention may range in length from about 15 nucleotides to the full length of the coding target or non-coding target. In one embodiment of the invention, the polynucleotide probes are at least about 15 nucleotides in length. In another embodiment, the polynucleotide probes are at least about 20 nucleotides in length. In a further embodiment, the polynucleotide probes are at least about 25 nucleotides in length. In another embodiment, the polynucleotide probes are between about 15 nucleotides and about 500 nucleotides in length. In other embodiments, the polynucleotide probes are between about 15 nucleotides and about 450 nucleotides, about 15 nucleotides and about 400 nucleotides, about 15 nucleotides and about 350 nucleotides, about 15 nucleotides and about 300 nucleotides, about 15 nucleotides and about 250 nucleotides, about 15 nucleotides and about 200 nucleotides in length. In some embodiments, the probes are at least 15 nucleotides in length. In some embodiments, the probes are at least 15 nucleotides in length. In some embodiments, the probes are at least 20 nucleotides, at least 25 nucleotides, at least 50 nucleotides, at least 75 nucleotides, at least 100 nucleotides, at least 125 nucleotides, at least 150 nucleotides, at least 200 nucleotides, at least 225 nucleotides, at least 250 nucleotides, at least 275 nucleotides, at least 300 nucleotides, at least 325 nucleotides, at least 350 nucleotides, at least 375 nucleotides in length.

The polynucleotide probes of a probe set can comprise RNA, DNA, RNA or DNA mimetics, or combinations thereof, and can be single-stranded or double-stranded. Thus the polynucleotide probes can be composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotide probes having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotide probes may provide desirable properties such as, for example, enhanced affinity for a target gene and increased stability. The probe set may comprise a probe that hybridizes to or corresponds to a coding target and/or a non-coding target. Preferably, the probe set comprises a plurality of probes that hybridizes to or corresponds to a combination of a coding target and non-coding target.

The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 5 coding targets and/or non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 10 coding targets and/or non-coding targets. The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 15 coding targets and/or non-coding targets. In some instances, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 20 coding targets and/or non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 30 coding targets and/or non-coding targets. The probe set can comprise a plurality of probes that hybridizes to or corresponds to at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 coding targets and/or non-coding targets.

The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 5 non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 10 non-coding targets. The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 15 non-coding targets. In some instances, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 20 non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 30 non-coding targets. The probe set can comprise a plurality of probes that hybridizes to or corresponds to at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 non-coding targets.

The probe set may comprise a plurality of probes, wherein at least about 5% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 8% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 10% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 12% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 15% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 18% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 20% of the plurality of probes hybridize to or correspond to non-coding targets. In some instances, the probe set comprises a plurality of probes, wherein at least about 25% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 30% of the plurality of probes hybridize to or correspond to non-coding targets. Alternatively, the probe set comprises a plurality of probes, wherein at least about 35% of the plurality of probes hybridize to or correspond to non-coding targets. In some instances, the probe set comprises a plurality of probes, wherein at least about 40% of the plurality of probes hybridize to or correspond to non-coding targets. In other instances, the probe set comprises a plurality of probes, wherein at least about 45% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 50% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 55% of the plurality of probes hybridize to or correspond to non-coding targets. Alternatively, the probe set comprises a plurality of probes, wherein at least about 60% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 65% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 70% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 75% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 80% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 85% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 90% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 95% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 97% of the plurality of probes hybridize to or correspond to non-coding targets.

The probe set can comprise a plurality of probes, wherein less than about 95% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 90% of the plurality of probes hybridize to or correspond to coding targets. Alternatively, the probe set comprises a plurality of probes, wherein less than about 85% of the plurality of probes hybridize to or correspond to coding targets. In some instances, the probe set comprises a plurality of probes, wherein less than about 80% of the plurality of probes hybridize to or correspond to coding targets. In other instances, the probe set comprises a plurality of probes, wherein less than about 75% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 70% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 65% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 60% of the plurality of probes hybridize to or correspond to coding targets. In some instances, the probe set comprises a plurality of probes, wherein less than about 55% of the plurality of probes hybridize to or correspond to coding targets. In other instances, the probe set comprises a plurality of probes, wherein less than about 50% of the plurality of probes hybridize to or correspond to coding targets. Alternatively, the probe set comprises a plurality of probes, wherein less than about 945% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 40% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 35% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 30% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 25% of the plurality of probes hybridize to or correspond to coding targets. In some instances, the probe set comprises a plurality of probes, wherein less than about 20% of the plurality of probes hybridize to or correspond to coding targets. In other instances, the probe set comprises a plurality of probes, wherein less than about 15% of the plurality of probes hybridize to or correspond to coding targets. Alternatively, the probe set comprises a plurality of probes, wherein less than about 12% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 10% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 8% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 5% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 3% of the plurality of probes hybridize to or correspond to coding targets.

The probe set may comprise a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% specificity. In some embodiments, the probe set further comprises a probe capable of detecting an expression level of at least one coding target. The probe set can comprise any of the probe sets as disclosed in Tables 17, 19, 22-24, and 27-30 (see ‘Probe set ID’ column). In some instances, the probe set comprises probe set ID 2518027. Alternatively, the probe set comprises probe set ID 3046448; 3046449; 3046450; 3046457; 3046459; 3046460; 3046461; 3046462; 3046465; 3956596; 3956601; 3956603; 3103704; 3103705; 3103706; 3103707; 3103708; 3103710; 3103712; 3103713; 3103714; 3103715; 3103717; 3103718; 3103720; 3103721; 3103725; 3103726; 2719689; 2719692; 2719694; 2719695; 2719696; 2642733; 2642735; 2642738; 2642739; 2642740; 2642741; 2642744; 2642745; 2642746; 2642747; 2642748; 2642750; 2642753; 3970026; 3970034; 3970036; 3970039; 2608321; 2608324; 2608326; 2608331; 2608332; 2536222; 2536226; 2536228; 2536229; 2536231; 2536232; 2536233; 2536234; 2536235; 2536236; 2536237; 2536238; 2536240; 2536241; 2536243; 2536245; 2536248; 2536249; 2536252; 2536253; 2536256; 2536260; 2536261; 2536262; 3670638; 3670639; 3670641; 3670644; 3670645; 3670650; 3670659; 3670660; 3670661; 3670666, a complement thereof, a reverse complement thereof, or any combination thereof.

Further disclosed herein, is a classifier for use in diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The classifier may comprise a classifier as disclosed in Table 17. The classifier can comprise a classifier as disclosed in Table 19. The classifier can comprise the GLM2, KNN12, KNN16, NB20, SVM5, SVM11, SVM20 classifiers or any combination thereof. The classifier can comprise a GLM2 classifier. Alternatively, the classifier comprises a KNN12 classifier. The classifier can comprise a KNN16 classifier. In other instances, the classifier comprises a NB20 classifier. The classifier may comprise a SVM5 classifier. In some instances, the classifier comprises a SVM11 classifier. Alternatively, the classifier comprises a SVM20 classifier. Alternatively, the classifier comprises one or more Inter-Correlated Expression (ICE) blocks disclosed herein. The classifier can comprise one or more probe sets disclosed herein.

The classifier may comprise at least about 5 coding targets and/or non-coding targets. Alternatively, the classifier comprises at least about 10 coding targets and/or non-coding targets. The classifier may comprise at least about 15 coding targets and/or non-coding targets. In some instances, the classifier comprises at least about 20 coding targets and/or non-coding targets. Alternatively, the classifier comprises at least about 30 coding targets and/or non-coding targets. The classifier can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 coding targets and/or non-coding targets.

The classifier may comprise at least about 5 non-coding targets. Alternatively, the classifier comprises at least about 10 non-coding targets. The classifier may comprise at least about 15 non-coding targets. In some instances, the classifier comprises at least about 20 non-coding targets. Alternatively, the classifier comprises at least about 30 non-coding targets. The classifier can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 non-coding targets.

The classifier may comprise at least about 5% non-coding targets. The classifier may comprise at least about 8% non-coding targets. The classifier may comprise at least about 10% non-coding targets. The classifier may comprise at least about 12% non-coding targets. The classifier may comprise at least about 15% non-coding targets. The classifier may comprise at least about 18% non-coding targets. The classifier may comprise at least about 20% non-coding targets. In some instances, the classifier comprises at least about 25% non-coding targets. The classifier may comprise at least about 30% non-coding targets. Alternatively, the classifier comprises at least about 35% non-coding targets. In some instances, the classifier comprises at least about 40% non-coding targets. In other instances, the classifier comprises at least about 45% non-coding targets. The classifier may comprise at least about 50% non-coding targets. The classifier may comprise at least about 55% non-coding targets. Alternatively, the classifier comprises at least about 60% non-coding targets. The classifier may comprise at least about 65% non-coding targets. The classifier may comprise at least about 70% non-coding targets. The classifier may comprise at least about 75% non-coding targets. The classifier may comprise at least about 80% non-coding targets. The classifier may comprise at least about 85% non-coding targets. The classifier may comprise at least about 90% non-coding targets. The classifier may comprise at least about 95% non-coding targets. The classifier may comprise at least about 97% non-coding targets.

The classifier can comprise less than about 95% coding targets. The classifier can comprise less than about 90% coding targets. Alternatively, the classifier comprises less than about 85% coding targets. In some instances, the classifier comprises less than about 80% coding targets. In other instances, the classifier comprises less than about 75% coding targets. The classifier can comprise less than about 70% coding targets. The classifier can comprise less than about 65% coding targets. The classifier can comprise less than about 60% coding targets. In some instances, the classifier comprises less than about 55% coding targets. In other instances, the classifier comprises less than about 50% coding targets. Alternatively, the classifier comprises less than about 45% coding targets. The classifier can comprise less than about 40% coding targets. The classifier can comprise less than about 35% coding targets. The classifier can comprise less than about 30% coding targets. The classifier can comprise less than about 25% coding targets. In some instances, the classifier comprises less than about 20% coding targets. In other instances, the classifier comprises less than about 15% coding targets. Alternatively, the classifier comprises less than about 12% coding targets. The classifier can comprise less than about 10% coding targets. The classifier can comprise less than about 8% coding targets. The classifier can comprise less than about 5% coding targets. The classifier can comprise less than about 3% coding targets.

Further disclosed herein, is an Inter-Correlated Expression (ICE) block for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The ICE block may comprise one or more ICE Block IDs as disclosed in Tables 22-24. The ICE block can comprise Block ID_2879, Block ID_2922, Block ID_4271, Block ID_4627, Block ID_5080, or any combination thereof. Alternatively, the ICE block comprises Block ID_6592, Block ID_4226, Block ID_6930, Block ID_7113, Block ID_5470, or any combination thereof. In other instances, the ICE block comprises Block ID_7716, Block ID_4271, Block ID_5000, Block ID_5986, Block ID_1146, Block ID_7640, Block ID_4308, Block ID_1532, Block ID_2922, or any combination thereof. The ICE block can comprise Block ID_2922. Alternatively, the ICE block comprises Block ID_5080. In other instances, the ICE block comprises Block ID_6592. The ICE block can comprise Block ID_4627. Alternatively, the ICE block comprises Block ID_7113. In some instances, the ICE block comprises Block ID_5470. In other instances, the ICE block comprises Block ID_5155. The ICE block can comprise Block ID_6371. Alternatively, the ICE block comprises Block ID_2879.

The ICE block may comprise at least about 5 coding targets and/or non-coding targets. Alternatively, the ICE block comprises at least about 10 coding targets and/or non-coding targets. The ICE block may comprise at least about 15 coding targets and/or non-coding targets. In some instances, the ICE block comprises at least about 20 coding targets and/or non-coding targets. Alternatively, the ICE block comprises at least about 30 coding targets and/or non-coding targets. The ICE block can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 coding targets and/or non-coding targets.

The ICE block may comprise at least about 5 non-coding targets. Alternatively, the ICE block comprises at least about 10 non-coding targets. The ICE block may comprise at least about 15 non-coding targets. In some instances, the ICE block comprises at least about 20 non-coding targets. Alternatively, the ICE block comprises at least about 30 non-coding targets. The ICE block can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 non-coding targets.

The ICE block may comprise at least about 5% non-coding targets. The ICE block may comprise at least about 8% non-coding targets. The ICE block may comprise at least about 10% non-coding targets. The ICE block may comprise at least about 12% non-coding targets. The ICE block may comprise at least about 15% non-coding targets. The ICE block may comprise at least about 18% non-coding targets. The ICE block may comprise at least about 20% non-coding targets. In some instances, the ICE block comprises at least about 25% non-coding targets. The ICE block may comprise at least about 30% non-coding targets. Alternatively, the ICE block comprises at least about 35% non-coding targets. In some instances, the ICE block comprises at least about 40% non-coding targets. In other instances, the ICE block comprises at least about 45% non-coding targets. The ICE block may comprise at least about 50% non-coding targets. The ICE block may comprise at least about 55% non-coding targets. Alternatively, the ICE block comprises at least about 60% non-coding targets. The ICE block may comprise at least about 65% non-coding targets. The ICE block may comprise at least about 70% non-coding targets. The ICE block may comprise at least about 75% non-coding targets. The ICE block may comprise at least about 80% non-coding targets. The ICE block may comprise at least about 85% non-coding targets. The ICE block may comprise at least about 90% non-coding targets. The ICE block may comprise at least about 95% non-coding targets. The ICE block may comprise at least about 97% non-coding targets.

The ICE block can comprise less than about 95% coding targets. The ICE block can comprise less than about 90% coding targets. Alternatively, the ICE block comprises less than about 85% coding targets. In some instances, the ICE block comprises less than about 80% coding targets. In other instances, the ICE block comprises less than about 75% coding targets. The ICE block can comprise less than about 70% coding targets. The ICE block can comprise less than about 65% coding targets. The ICE block can comprise less than about 60% coding targets. In some instances, the ICE block comprises less than about 55% coding targets. In other instances, the ICE block comprises less than about 50% coding targets. Alternatively, the ICE block comprises less than about 45% coding targets. The ICE block can comprise less than about 40% coding targets. The ICE block can comprise less than about 35% coding targets. The ICE block can comprise less than about 30% coding targets. The ICE block can comprise less than about 25% coding targets. In some instances, the ICE block comprises less than about 20% coding targets. In other instances, the ICE block comprises less than about 15% coding targets. Alternatively, the ICE block comprises less than about 12% coding targets. The ICE block can comprise less than about 10% coding targets. The ICE block can comprise less than about 8% coding targets. The ICE block can comprise less than about 5% coding targets. The ICE block can comprise less than about 3% coding targets.

Further disclosed herein, is a digital Gleason score predictor for prognosing the risk of biochemical recurrence. The digital Gleason score predictor can comprise a classifier. The classifier can comprise at least one non-coding target. In some instances, the classifier further comprises at least one coding-target. In some instances, the digital Gleason score predictor comprises a plurality of targets, wherein the plurality of targets comprise at least one coding target and at least one non-coding target. The non-coding target, coding target and plurality of targets can be any of the targets disclosed herein. The targets can be selected from any of Tables 4, 6-9, 15, 16, 17, 19, 22-24, and 26-30. The targets can comprise a sequence comprising at least a portion of any of SEQ ID NOs.: 1-903. In some instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, 99% or 100%. The accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence can be at least about 50%. Alternatively, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 55%. In some instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 60%. In other instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 65%. The accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence can be at least about 70%. Alternatively, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 75%. In some instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 80%. In other instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 85%.

In some instances, the probe sets, PSRs, ICE blocks, and classifiers disclosed herein are clinically significant. In some instances, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the AUC value. In order to be clinically significant, the AUC value is at least about 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, or 0.95. The clinical significant of the probe sets, PSRs, ICE blocks, and classifiers can be determined by the percent accuracy. For example, a probe set, PSR, ICE block, and/or classifier is determined to be clinically significant if the accuracy of the probe set, PSR, ICE block and/or classifier is at least about 50%, 55%, 60%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 84%, 86%, 88%, 90%, 92%, 94%, 96%, or 98%. In other instances, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the median fold difference (MDF) value. In order to be clinically significant, the MDF value is at least about 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, or 2.0. In some instances, the MDF value is greater than or equal to 1.1. In other instances, the MDF value is greater than or equal to 1.2. Alternatively, or additionally, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the t-test P-value. In some instances, in order to be clinically significant, the t-test P-value is less than about 0.070, 0.065, 0.060, 0.055, 0.050, 0.045, 0.040, 0.035, 0.030, 0.025, 0.020, 0.015, 0.010, 0.005, 0.004, or 0.003. The t-test P-value can be less than about 0.050. Alternatively, the t-test P-value is less than about 0.010. In some instances, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the clinical outcome. For example, different clinical outcomes can have different minimum or maximum thresholds for AUC values, MDF values, t-test P-values, and accuracy values that would determine whether the probe set, PSR, ICE block, and/or classifier is clinically significant. In another example, a probe set, PSR, ICE block, or classifier can be considered clinically significant if the P-value of the t-test was lower than about 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01 in any of the following comparisons: BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM. Additionally, a probe set, PSR, ICE block, or classifier is determined to be clinically significant if the P-values of the differences between the KM curves for BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM is lower than about 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01.

The system of the present invention further provides for primers and primer pairs capable of amplifying target sequences defined by the probe set, or fragments or subsequences or complements thereof. The nucleotide sequences of the probe set may be provided in computer-readable media for in silico applications and as a basis for the design of appropriate primers for amplification of one or more target sequences of the probe set.

Primers based on the nucleotide sequences of target sequences can be designed for use in amplification of the target sequences. For use in amplification reactions such as PCR, a pair of primers can be used. The exact composition of the primer sequences is not critical to the invention, but for most applications the primers may hybridize to specific sequences of the probe set under stringent conditions, particularly under conditions of high stringency, as known in the art. The pairs of primers are usually chosen so as to generate an amplification product of at least about 50 nucleotides, more usually at least about 100 nucleotides. Algorithms for the selection of primer sequences are generally known, and are available in commercial software packages. These primers may be used in standard quantitative or qualitative PCR-based assays to assess transcript expression levels of RNAs defined by the probe set. Alternatively, these primers may be used in combination with probes, such as molecular beacons in amplifications using real-time PCR.

In one embodiment, the primers or primer pairs, when used in an amplification reaction, specifically amplify at least a portion of a nucleic acid depicted in one of Table 6 (or subgroups thereof as set forth herein), an RNA form thereof, or a complement to either thereof.

As is known in the art, a nucleoside is a base-sugar combination and a nucleotide is a nucleoside that further includes a phosphate group covalently linked to the sugar portion of the nucleoside. In forming oligonucleotides, the phosphate groups covalently link adjacent nucleosides to one another to form a linear polymeric compound, with the normal linkage or backbone of RNA and DNA being a 3′ to 5′ phosphodiester linkage. Specific examples of polynucleotide probes or primers useful in this invention include oligonucleotides containing modified backbones or non-natural internucleoside linkages. As defined in this specification, oligonucleotides having modified backbones include both those that retain a phosphorus atom in the backbone and those that lack a phosphorus atom in the backbone. For the purposes of the present invention, and as sometimes referenced in the art, modified oligonucleotides that do not have a phosphorus atom in their internucleoside backbone can also be considered to be oligonucleotides.

Exemplary polynucleotide probes or primers having modified oligonucleotide backbones include, for example, those with one or more modified internucleotide linkages that are phosphorothioates, chiral phosphorothioates, phosphorodithioates, phosphotriesters, aminoalkylphosphotriesters, methyl and other alkyl phosphonates including 3′-alkylene phosphonates and chiral phosphonates, phosphinates, phosphoramidates including 3′amino phosphoramidate and aminoalkylphosphoramidates, thionophosphoramidates, thionoalkylphosphonates, thionoalkylphosphotriesters, and boranophosphates having normal 3′-5′ linkages, 2′-5′ linked analogs of these, and those having inverted polarity wherein the adjacent pairs of nucleoside units are linked 3′-5′ to 5′-3′ or 2′-5′ to 5′-2′. Various salts, mixed salts and free acid forms are also included.

Exemplary modified oligonucleotide backbones that do not include a phosphorus atom are formed by short chain alkyl or cycloalkyl internucleoside linkages, mixed heteroatom and alkyl or cycloalkyl internucleoside linkages, or one or more short chain heteroatomic or heterocyclic internucleoside linkages. Such backbones include morpholino linkages (formed in part from the sugar portion of a nucleoside); siloxane backbones; sulfide, sulfoxide and sulphone backbones; formacetyl and thioformacetyl backbones; methylene formacetyl and thioformacetyl backbones; alkene containing backbones; sulphamate backbones; methyleneimino and methylenehydrazino backbones; sulphonate and sulfonamide backbones; amide backbones; and others having mixed N, 0, S and CH₂ component parts.

The present invention also contemplates oligonucleotide mimetics in which both the sugar and the internucleoside linkage of the nucleotide units are replaced with novel groups. The base units are maintained for hybridization with an appropriate nucleic acid target compound. An example of such an oligonucleotide mimetic, which has been shown to have excellent hybridization properties, is a peptide nucleic acid (PNA). In PNA compounds, the sugar-backbone of an oligonucleotide is replaced with an amide containing backbone, in particular an aminoethylglycine backbone. The nucleobases are retained and are bound directly or indirectly to aza-nitrogen atoms of the amide portion of the backbone.

The present invention also contemplates polynucleotide probes or primers comprising “locked nucleic acids” (LNAs), which may be novel conformationally restricted oligonucleotide analogues containing a methylene bridge that connects the 2′-O of ribose with the 4′-C. LNA and LNA analogues may display very high duplex thermal stabilities with complementary DNA and RNA, stability towards 3′-exonuclease degradation, and good solubility properties. Synthesis of the LNA analogues of adenine, cytosine, guanine, 5-methylcytosine, thymine and uracil, their oligomerization, and nucleic acid recognition properties have been described. Studies of mismatched sequences show that LNA obey the Watson-Crick base pairing rules with generally improved selectivity compared to the corresponding unmodified reference strands.

LNAs may form duplexes with complementary DNA or RNA or with complementary LNA, with high thermal affinities. The universality of LNA-mediated hybridization has been emphasized by the formation of exceedingly stable LNA:LNA duplexes. LNA:LNA hybridization was shown to be the most thermally stable nucleic acid type duplex system, and the RNA-mimicking character of LNA was established at the duplex level. Introduction of three LNA monomers (T or A) resulted in significantly increased melting points toward DNA complements.

Synthesis of 2′-amino-LNA and 2′-methylamino-LNA has been described and thermal stability of their duplexes with complementary RNA and DNA strands reported. Preparation of phosphorothioate-LNA and 2′-thio-LNA have also been described.

Modified polynucleotide probes or primers may also contain one or more substituted sugar moieties. For example, oligonucleotides may comprise sugars with one of the following substituents at the 2′ position: OH; F; O-, S-, or N-alkyl; O-, S-, or N-alkenyl; 0-, S- or N-alkynyl; or O-alkyl-O-alkyl, wherein the alkyl, alkenyl and alkynyl may be substituted or unsubstituted C₁ to C₁₀ alkyl or C2 to C₁₀ alkenyl and alkynyl. Examples of such groups are: O[(CH₂)_(n)O]_(m)CH₃, O(CH₂)_(n)OCH₃, O(CH₂)_(n)NH₂, O(CH₂)_(n)CH₃ONH₂, and O(CH₂)_(n)ON[((CH₂)_(n)CH₃)]₂, where n and m are from 1 to about 10. Alternatively, the oligonucleotides may comprise one of the following substituents at the 2′ position: C₁ to C₁₀ lower alkyl, substituted lower alkyl, alkaryl, aralkyl, O-alkaryl or O-aralkyl, SH, SCH₃, OCN, Cl, Br, CN, CF₃, OCF₃, SOCH₃, SO₂CH₃, ONO₂, NO₂, N₃, NH₂, heterocycloalkyl, heterocycloalkaryl, aminoalkylamino, polyalkylamino, substituted silyl, an RNA cleaving group, a reporter group, an intercalator, a group for improving the pharmacokinetic properties of an oligonucleotide, or a group for improving the pharmacodynamic properties of an oligonucleotide, and other substituents having similar properties. Specific examples include 2′-methoxyethoxy (2′-O—CH₂CH₂OCH₃, also known as 2′-O-(2-methoxyethyl) or 2′-MOE), 2′-dimethylaminooxyethoxy (O(CH2)2ON(CH₃)₂ group, also known as 2′-DMA0E), 2′-methoxy (2′-O—CH₃), 2′-aminopropoxy (2′-OCH₂CH₂CH₂NH₂) and 2′-fluoro (2′-F).

Similar modifications may also be made at other positions on the polynucleotide probes or primers, particularly the 3′ position of the sugar on the 3′ terminal nucleotide or in 2′-5′ linked oligonucleotides and the 5′ position of 5′ terminal nucleotide. Polynucleotide probes or primers may also have sugar mimetics such as cyclobutyl moieties in place of the pentofuranosyl sugar.

Polynucleotide probes or primers may also include modifications or substitutions to the nucleobase. As used herein, “unmodified” or “natural” nucleobases include the purine bases adenine (A) and guanine (G), and the pyrimidine bases thymine (T), cytosine (C) and uracil (U).

Modified nucleobases include other synthetic and natural nucleobases such as 5-methylcytosine (5-me-C), 5-hydroxymethyl cytosine, xanthine, hypoxanthine, 2-aminoadenine, 6-methyl and other alkyl derivatives of adenine and guanine, 2-propyl and other alkyl derivatives of adenine and guanine, 2-thiouracil, 2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl uracil and cytosine, 6-azo uracil, cytosine and thymine, 5-uracil (pseudouracil), 4-thiouracil, 8-halo, 8-amino, 8-thiol, 8-thioalkyl, 8-hydroxyl and other 8-substituted adenines and guanines, 5-halo particularly 5-bromo, 5-trifluoromethyl and other 5-substituted uracils and cytosines, 7-methylguanine and 7-methyladenine, 8-azaguanine and 8-azaadenine, 7-deazaguanine and 7-deazaadenine and 3-deazaguanine and 3-deazaadenine. Further nucleobases include those disclosed in U.S. Pat. No. 3,687,808; The Concise Encyclopedia Of Polymer Science And Engineering, (1990) pp 858-859, Kroschwitz, J. I., ed. John Wiley & Sons; Englisch et al., Angewandte Chemie, Int. Ed., 30:613 (1991); and Sanghvi, Y. S., (1993) Antisense Research and Applications, pp 289-302, Crooke, S. T. and Lebleu, B., ed., CRC Press. Certain of these nucleobases are particularly useful for increasing the binding affinity of the polynucleotide probes of the invention. These include 5-substituted pyrimidines, 6-azapyrimidines and N-2, N-6 and O-6 substituted purines, including 2-aminopropyladenine, 5-propynyluracil and 5-propynylcytosine. 5-methylcytosine substitutions have been shown to increase nucleic acid duplex stability by 0.6-1.2° C.

One skilled in the art recognizes that it is not necessary for all positions in a given polynucleotide probe or primer to be uniformly modified. The present invention, therefore, contemplates the incorporation of more than one of the aforementioned modifications into a single polynucleotide probe or even at a single nucleoside within the probe or primer.

One skilled in the art also appreciates that the nucleotide sequence of the entire length of the polynucleotide probe or primer does not need to be derived from the target sequence. Thus, for example, the polynucleotide probe may comprise nucleotide sequences at the 5′ and/or 3′ termini that are not derived from the target sequences. Nucleotide sequences which are not derived from the nucleotide sequence of the target sequence may provide additional functionality to the polynucleotide probe. For example, they may provide a restriction enzyme recognition sequence or a “tag” that facilitates detection, isolation, purification or immobilization onto a solid support. Alternatively, the additional nucleotides may provide a self-complementary sequence that allows the primer/probe to adopt a hairpin configuration. Such configurations are necessary for certain probes, for example, molecular beacon and Scorpion probes, which can be used in solution hybridization techniques.

The polynucleotide probes or primers can incorporate moieties useful in detection, isolation, purification, or immobilization, if desired. Such moieties are well-known in the art (see, for example, Ausubel et al., (1997 & updates) Current Protocols in Molecular Biology, Wiley & Sons, New York) and are chosen such that the ability of the probe to hybridize with its target sequence is not affected.

Examples of suitable moieties are detectable labels, such as radioisotopes, fluorophores, chemiluminophores, enzymes, colloidal particles, and fluorescent microparticles, as well as antigens, antibodies, haptens, avidin/streptavidin, biotin, haptens, enzyme cofactors/substrates, enzymes, and the like.

A label can optionally be attached to or incorporated into a probe or primer polynucleotide to allow detection and/or quantitation of a target polynucleotide representing the target sequence of interest. The target polynucleotide may be the expressed target sequence RNA itself, a cDNA copy thereof, or an amplification product derived therefrom, and may be the positive or negative strand, so long as it can be specifically detected in the assay being used. Similarly, an antibody may be labeled.

In certain multiplex formats, labels used for detecting different targets may be distinguishable. The label can be attached directly (e.g., via covalent linkage) or indirectly, e.g., via a bridging molecule or series of molecules (e.g., a molecule or complex that can bind to an assay component, or via members of a binding pair that can be incorporated into assay components, e.g. biotin-avidin or streptavidin). Many labels are commercially available in activated forms which can readily be used for such conjugation (for example through amine acylation), or labels may be attached through known or determinable conjugation schemes, many of which are known in the art.

Labels useful in the invention described herein include any substance which can be detected when bound to or incorporated into the biomolecule of interest. Any effective detection method can be used, including optical, spectroscopic, electrical, piezoelectrical, magnetic, Raman scattering, surface plasmon resonance, colorimetric, calorimetric, etc. A label is typically selected from a chromophore, a lumiphore, a fluorophore, one member of a quenching system, a chromogen, a hapten, an antigen, a magnetic particle, a material exhibiting nonlinear optics, a semiconductor nanocrystal, a metal nanoparticle, an enzyme, an antibody or binding portion or equivalent thereof, an aptamer, and one member of a binding pair, and combinations thereof. Quenching schemes may be used, wherein a quencher and a fluorophore as members of a quenching pair may be used on a probe, such that a change in optical parameters occurs upon binding to the target introduce or quench the signal from the fluorophore. One example of such a system is a molecular beacon. Suitable quencher/fluorophore systems are known in the art. The label may be bound through a variety of intermediate linkages. For example, a polynucleotide may comprise a biotin-binding species, and an optically detectable label may be conjugated to biotin and then bound to the labeled polynucleotide. Similarly, a polynucleotide sensor may comprise an immunological species such as an antibody or fragment, and a secondary antibody containing an optically detectable label may be added.

Chromophores useful in the methods described herein include any substance which can absorb energy and emit light. For multiplexed assays, a plurality of different signaling chromophores can be used with detectably different emission spectra. The chromophore can be a lumophore or a fluorophore. Typical fluorophores include fluorescent dyes, semiconductor nanocrystals, lanthanide chelates, polynucleotide-specific dyes and green fluorescent protein.

Coding schemes may optionally be used, comprising encoded particles and/or encoded tags associated with different polynucleotides of the invention. A variety of different coding schemes are known in the art, including fluorophores, including SCNCs, deposited metals, and RF tags.

Polynucleotides from the described target sequences may be employed as probes for detecting target sequences expression, for ligation amplification schemes, or may be used as primers for amplification schemes of all or a portion of a target sequences. When amplified, either strand produced by amplification may be provided in purified and/or isolated form.

In one embodiment, polynucleotides of the invention include (a) a nucleic acid depicted in Table 6; (b) an RNA form of any one of the nucleic acids depicted in Table 6; (c) a peptide nucleic acid form of any of the nucleic acids depicted in Table 6; (d) a nucleic acid comprising at least 20 consecutive bases of any of (a-c); (e) a nucleic acid comprising at least 25 bases having at least 90% sequenced identity to any of (a-c); and (f) a complement to any of (a-e).

Complements may take any polymeric form capable of base pairing to the species recited in (a)-(e), including nucleic acid such as RNA or DNA, or may be a neutral polymer such as a peptide nucleic acid. Polynucleotides of the invention can be selected from the subsets of the recited nucleic acids described herein, as well as their complements.

In some embodiments, polynucleotides of the invention comprise at least 20 consecutive bases of the nucleic acids as depicted in Table 6 or a complement thereto. The polynucleotides may comprise at least 21, 22, 23, 24, 25, 27, 30, 32, 35 or more consecutive bases of the nucleic acid sequences as depicted in Table 6, as applicable.

The polynucleotides may be provided in a variety of formats, including as solids, in solution, or in an array. The polynucleotides may optionally comprise one or more labels, which may be chemically and/or enzymatically incorporated into the polynucleotide.

In one embodiment, solutions comprising polynucleotide and a solvent are also provided. In some embodiments, the solvent may be water or may be predominantly aqueous. In some embodiments, the solution may comprise at least two, three, four, five, six, seven, eight, nine, ten, twelve, fifteen, seventeen, twenty or more different polynucleotides, including primers and primer pairs, of the invention. Additional substances may be included in the solution, alone or in combination, including one or more labels, additional solvents, buffers, biomolecules, polynucleotides, and one or more enzymes useful for performing methods described herein, including polymerases and ligases. The solution may further comprise a primer or primer pair capable of amplifying a polynucleotide of the invention present in the solution.

In some embodiments, one or more polynucleotides provided herein can be provided on a substrate. The substrate can comprise a wide range of material, either biological, nonbiological, organic, inorganic, or a combination of any of these. For example, the substrate may be a polymerized Langmuir Blodgett film, functionalized glass, Si, Ge, GaAs, GaP, SiO₂, SiN₄, modified silicon, or any one of a wide variety of gels or polymers such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, cross-linked polystyrene, polyacrylic, polylactic acid, polyglycolic acid, poly(lactide coglycolide), polyanhydrides, poly(methyl methacrylate), poly(ethylene-co-vinyl acetate), polysiloxanes, polymeric silica, latexes, dextran polymers, epoxies, polycarbonates, or combinations thereof. Conducting polymers and photoconductive materials can be used.

Substrates can be planar crystalline substrates such as silica based substrates (e.g. glass, quartz, or the like), or crystalline substrates used in, e.g., the semiconductor and microprocessor industries, such as silicon, gallium arsenide, indium doped GaN and the like, and include semiconductor nanocrystals.

The substrate can take the form of an array, a photodiode, an optoelectronic sensor such as an optoelectronic semiconductor chip or optoelectronic thin-film semiconductor, or a biochip. The location(s) of probe(s) on the substrate can be addressable; this can be done in highly dense formats, and the location(s) can be microaddressable or nanoaddressable.

Silica aerogels can also be used as substrates, and can be prepared by methods known in the art. Aerogel substrates may be used as free standing substrates or as a surface coating for another substrate material.

The substrate can take any form and typically is a plate, slide, bead, pellet, disk, particle, microparticle, nanoparticle, strand, precipitate, optionally porous gel, sheets, tube, sphere, container, capillary, pad, slice, film, chip, multiwell plate or dish, optical fiber, etc. The substrate can be any form that is rigid or semi-rigid. The substrate may contain raised or depressed regions on which an assay component is located. The surface of the substrate can be etched using known techniques to provide for desired surface features, for example trenches, v-grooves, mesa structures, or the like.

Surfaces on the substrate can be composed of the same material as the substrate or can be made from a different material, and can be coupled to the substrate by chemical or physical means. Such coupled surfaces may be composed of any of a wide variety of materials, for example, polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, membranes, or any of the above-listed substrate materials. The surface can be optically transparent and can have surface Si—OH functionalities, such as those found on silica surfaces.

The substrate and/or its optional surface can be chosen to provide appropriate characteristics for the synthetic and/or detection methods used. The substrate and/or surface can be transparent to allow the exposure of the substrate by light applied from multiple directions. The substrate and/or surface may be provided with reflective “mirror” structures to increase the recovery of light.

The substrate and/or its surface is generally resistant to, or is treated to resist, the conditions to which it is to be exposed in use, and can be optionally treated to remove any resistant material after exposure to such conditions.

The substrate or a region thereof may be encoded so that the identity of the sensor located in the substrate or region being queried may be determined. Any suitable coding scheme can be used, for example optical codes, RFID tags, magnetic codes, physical codes, fluorescent codes, and combinations of codes.

Preparation of Probes and Primers

The polynucleotide probes or primers of the present invention can be prepared by conventional techniques well-known to those skilled in the art. For example, the polynucleotide probes can be prepared using solid-phase synthesis using commercially available equipment. As is well-known in the art, modified oligonucleotides can also be readily prepared by similar methods. The polynucleotide probes can also be synthesized directly on a solid support according to methods standard in the art. This method of synthesizing polynucleotides is particularly useful when the polynucleotide probes are part of a nucleic acid array.

Polynucleotide probes or primers can be fabricated on or attached to the substrate by any suitable method, for example the methods described in U.S. Pat. No. 5,143,854, PCT Publ. No. WO 92/10092, U.S. patent application Ser. No. 07/624,120, filed Dec. 6, 1990 (now abandoned), Fodor et al., Science, 251: 767-777 (1991), and PCT Publ. No. WO 90/15070). Techniques for the synthesis of these arrays using mechanical synthesis strategies are described in, e.g., PCT Publication No. WO 93/09668 and U.S. Pat. No. 5,384,261. Still further techniques include bead based techniques such as those described in PCT Appl. No. PCT/US93/04145 and pin based methods such as those described in U.S. Pat. No. 5,288,514. Additional flow channel or spotting methods applicable to attachment of sensor polynucleotides to a substrate are described in U.S. patent application Ser. No. 07/980,523, filed Nov. 20, 1992, and U.S. Pat. No. 5,384,261.

Alternatively, the polynucleotide probes of the present invention can be prepared by enzymatic digestion of the naturally occurring target gene, or mRNA or cDNA derived therefrom, by methods known in the art.

Diagnostic Samples

Diagnostic samples for use with the systems and in the methods of the present invention comprise nucleic acids suitable for providing RNA expression information. In principle, the biological sample from which the expressed RNA is obtained and analyzed for target sequence expression can be any material suspected of comprising cancer tissue or cells. The diagnostic sample can be a biological sample used directly in a method of the invention. Alternatively, the diagnostic sample can be a sample prepared from a biological sample.

In one embodiment, the sample or portion of the sample comprising or suspected of comprising cancer tissue or cells can be any source of biological material, including cells, tissue, secretions, or fluid, including bodily fluids. Non-limiting examples of the source of the sample include an aspirate, a needle biopsy, a cytology pellet, a bulk tissue preparation or a section thereof obtained for example by surgery or autopsy, lymph fluid, blood, plasma, serum, tumors, and organs. Alternatively, or additionally, the source of the sample can be urine, bile, excrement, sweat, tears, vaginal fluids, spinal fluid, and stool. In some instances, the sources of the sample are secretions. In some instances, the secretions are exosomes.

The samples may be archival samples, having a known and documented medical outcome, or may be samples from current patients whose ultimate medical outcome is not yet known.

In some embodiments, the sample may be dissected prior to molecular analysis. The sample may be prepared via macrodissection of a bulk tumor specimen or portion thereof, or may be treated via microdissection, for example via Laser Capture Microdissection (LCM).

The sample may initially be provided in a variety of states, as fresh tissue, fresh frozen tissue, fine needle aspirates, and may be fixed or unfixed. Frequently, medical laboratories routinely prepare medical samples in a fixed state, which facilitates tissue storage. A variety of fixatives can be used to fix tissue to stabilize the morphology of cells, and may be used alone or in combination with other agents. Exemplary fixatives include crosslinking agents, alcohols, acetone, Bouin's solution, Zenker solution, Hely solution, osmic acid solution and Carnoy solution.

Crosslinking fixatives can comprise any agent suitable for forming two or more covalent bonds, for example, an aldehyde. Sources of aldehydes typically used for fixation include formaldehyde, paraformaldehyde, glutaraldehyde or formalin. Preferably, the crosslinking agent comprises formaldehyde, which may be included in its native form or in the form of paraformaldehyde or formalin. One of skill in the art would appreciate that for samples in which crosslinking fixatives have been used special preparatory steps may be necessary including for example heating steps and proteinase-k digestion; see methods.

One or more alcohols may be used to fix tissue, alone or in combination with other fixatives. Exemplary alcohols used for fixation include methanol, ethanol and isopropanol.

Formalin fixation is frequently used in medical laboratories. Formalin comprises both an alcohol, typically methanol, and formaldehyde, both of which can act to fix a biological sample.

Whether fixed or unfixed, the biological sample may optionally be embedded in an embedding medium. Exemplary embedding media used in histology including paraffin, Tissue-Tek® V.I.P.™, Paramat, Paramat Extra, Paraplast, Paraplast X-tra, Paraplast Plus, Peel Away Paraffin Embedding Wax, Polyester Wax, Carbowax Polyethylene Glycol, Polyfin™, Tissue Freezing Medium TFMFM, Cryo-Gef™, and OCT Compound (Electron Microscopy Sciences, Hatfield, Pa.). Prior to molecular analysis, the embedding material may be removed via any suitable techniques, as known in the art. For example, where the sample is embedded in wax, the embedding material may be removed by extraction with organic solvent(s), for example xylenes. Kits are commercially available for removing embedding media from tissues. Samples or sections thereof may be subjected to further processing steps as needed, for example serial hydration or dehydration steps.

In some embodiments, the sample is a fixed, wax-embedded biological sample. Frequently, samples from medical laboratories are provided as fixed, wax-embedded samples, most commonly as formalin-fixed, paraffin embedded (FFPE) tissues.

Whatever the source of the biological sample, the target polynucleotide that is ultimately assayed can be prepared synthetically (in the case of control sequences), but typically is purified from the biological source and subjected to one or more preparative steps. The RNA may be purified to remove or diminish one or more undesired components from the biological sample or to concentrate it. Conversely, where the RNA is too concentrated for the particular assay, it may be diluted.

RNA Extraction

RNA can be extracted and purified from biological samples using any suitable technique. A number of techniques are known in the art, and several are commercially available (e.g., FormaPure nucleic acid extraction kit, Agencourt Biosciences, Beverly Mass., High Pure FFPE RNA Micro Kit, Roche Applied Science, Indianapolis, Ind.). RNA can be extracted from frozen tissue sections using TRIzol (Invitrogen, Carlsbad, Calif.) and purified using RNeasy Protect kit (Qiagen, Valencia, Calif.). RNA can be further purified using DNAse I treatment (Ambion, Austin, Tex.) to eliminate any contaminating DNA. RNA concentrations can be made using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). RNA can be further purified to eliminate contaminants that interfere with cDNA synthesis by cold sodium acetate precipitation. RNA integrity can be evaluated by running electropherograms, and RNA integrity number (RIN, a correlative measure that indicates intactness of mRNA) can be determined using the RNA 6000 PicoAssay for the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).

Kits

Kits for performing the desired method(s) are also provided, and comprise a container or housing for holding the components of the kit, one or more vessels containing one or more nucleic acid(s), and optionally one or more vessels containing one or more reagents. The reagents include those described in the composition of matter section above, and those reagents useful for performing the methods described, including amplification reagents, and may include one or more probes, primers or primer pairs, enzymes (including polymerases and ligases), intercalating dyes, labeled probes, and labels that can be incorporated into amplification products.

In some embodiments, the kit comprises primers or primer pairs specific for those subsets and combinations of target sequences described herein. At least two, three, four or five primers or pairs of primers suitable for selectively amplifying the same number of target sequence-specific polynucleotides can be provided in kit form. In some embodiments, the kit comprises from five to fifty primers or pairs of primers suitable for amplifying the same number of target sequence-representative polynucleotides of interest.

In some embodiments, the primers or primer pairs of the kit, when used in an amplification reaction, specifically amplify a non-coding target, coding target, or non-exonic target described herein, at least a portion of a nucleic acid depicted in one of SEQ ID NOs.: 1-903, an RNA form thereof, or a complement to either thereof. The kit may include a plurality of such primers or primer pairs which can specifically amplify a corresponding plurality of different amplify a non-coding target, coding target, or non-exonic transcript described herein, nucleic acids depicted in one of SEQ ID NOs.: 1-903, RNA forms thereof, or complements thereto. At least two, three, four or five primers or pairs of primers suitable for selectively amplifying the same number of target sequence-specific polynucleotides can be provided in kit form. In some embodiments, the kit comprises from five to fifty primers or pairs of primers suitable for amplifying the same number of target sequence-representative polynucleotides of interest.

The reagents may independently be in liquid or solid form. The reagents may be provided in mixtures. Control samples and/or nucleic acids may optionally be provided in the kit. Control samples may include tissue and/or nucleic acids obtained from or representative of tumor samples from patients showing no evidence of disease, as well as tissue and/or nucleic acids obtained from or representative of tumor samples from patients that develop systemic cancer.

The nucleic acids may be provided in an array format, and thus an array or microarray may be included in the kit. The kit optionally may be certified by a government agency for use in prognosing the disease outcome of cancer patients and/or for designating a treatment modality.

Instructions for using the kit to perform one or more methods of the invention can be provided with the container, and can be provided in any fixed medium. The instructions may be located inside or outside the container or housing, and/or may be printed on the interior or exterior of any surface thereof. A kit may be in multiplex form for concurrently detecting and/or quantitating one or more different target polynucleotides representing the expressed target sequences.

Devices

Devices useful for performing methods of the invention are also provided. The devices can comprise means for characterizing the expression level of a target sequence of the invention, for example components for performing one or more methods of nucleic acid extraction, amplification, and/or detection. Such components may include one or more of an amplification chamber (for example a thermal cycler), a plate reader, a spectrophotometer, capillary electrophoresis apparatus, a chip reader, and or robotic sample handling components. These components ultimately can obtain data that reflects the expression level of the target sequences used in the assay being employed.

The devices may include an excitation and/or a detection means. Any instrument that provides a wavelength that can excite a species of interest and is shorter than the emission wavelength(s) to be detected can be used for excitation. Commercially available devices can provide suitable excitation wavelengths as well as suitable detection component.

Exemplary excitation sources include a broadband UV light source such as a deuterium lamp with an appropriate filter, the output of a white light source such as a xenon lamp or a deuterium lamp after passing through a monochromator to extract out the desired wavelength(s), a continuous wave (cw) gas laser, a solid state diode laser, or any of the pulsed lasers. Emitted light can be detected through any suitable device or technique; many suitable approaches are known in the art. For example, a fluorimeter or spectrophotometer may be used to detect whether the test sample emits light of a wavelength characteristic of a label used in an assay.

The devices typically comprise a means for identifying a given sample, and of linking the results obtained to that sample. Such means can include manual labels, barcodes, and other indicators which can be linked to a sample vessel, and/or may optionally be included in the sample itself, for example where an encoded particle is added to the sample. The results may be linked to the sample, for example in a computer memory that contains a sample designation and a record of expression levels obtained from the sample. Linkage of the results to the sample can also include a linkage to a particular sample receptacle in the device, which is also linked to the sample identity.

The devices also comprise a means for correlating the expression levels of the target sequences being studied with a prognosis of disease outcome. Such means may comprise one or more of a variety of correlative techniques, including lookup tables, algorithms, multivariate models, and linear or nonlinear combinations of expression models or algorithms. The expression levels may be converted to one or more likelihood scores, reflecting a likelihood that the patient providing the sample may exhibit a particular disease outcome. The models and/or algorithms can be provided in machine readable format and can optionally further designate a treatment modality for a patient or class of patients.

The device also comprises output means for outputting the disease status, prognosis and/or a treatment modality. Such output means can take any form which transmits the results to a patient and/or a healthcare provider, and may include a monitor, a printed format, or both. The device may use a computer system for performing one or more of the steps provided.

The methods disclosed herein may also comprise the transmission of data/information. For example, data/information derived from the detection and/or quantification of the target may be transmitted to another device and/or instrument. In some instances, the information obtained from an algorithm may also be transmitted to another device and/or instrument. Transmission of the data/information may comprise the transfer of data/information from a first source to a second source. The first and second sources may be in the same approximate location (e.g., within the same room, building, block, campus). Alternatively, first and second sources may be in multiple locations (e.g., multiple cities, states, countries, continents, etc).

Transmission of the data/information may comprise digital transmission or analog transmission. Digital transmission may comprise the physical transfer of data (a digital bit stream) over a point-to-point or point-to-multipoint communication channel. Examples of such channels are copper wires, optical fibres, wireless communication channels, and storage media. The data may be represented as an electromagnetic signal, such as an electrical voltage, radiowave, microwave, or infrared signal.

Analog transmission may comprise the transfer of a continuously varying analog signal. The messages can either be represented by a sequence of pulses by means of a line code (baseband transmission), or by a limited set of continuously varying wave forms (passband transmission), using a digital modulation method. The passband modulation and corresponding demodulation (also known as detection) can be carried out by modern equipment. According to the most common definition of digital signal, both baseband and passband signals representing bit-streams are considered as digital transmission, while an alternative definition only considers the baseband signal as digital, and passband transmission of digital data as a form of digital-to-analog conversion.

Amplification and Hybridization

Following sample collection and nucleic acid extraction, the nucleic acid portion of the sample comprising RNA that is or can be used to prepare the target polynucleotide(s) of interest can be subjected to one or more preparative reactions. These preparative reactions can include in vitro transcription (IVT), labeling, fragmentation, amplification and other reactions. mRNA can first be treated with reverse transcriptase and a primer to create cDNA prior to detection, quantitation and/or amplification; this can be done in vitro with purified mRNA or in situ, e.g., in cells or tissues affixed to a slide.

By “amplification” is meant any process of producing at least one copy of a nucleic acid, in this case an expressed RNA, and in many cases produces multiple copies. An amplification product can be RNA or DNA, and may include a complementary strand to the expressed target sequence. DNA amplification products can be produced initially through reverse translation and then optionally from further amplification reactions. The amplification product may include all or a portion of a target sequence, and may optionally be labeled. A variety of amplification methods are suitable for use, including polymerase-based methods and ligation-based methods. Exemplary amplification techniques include the polymerase chain reaction method (PCR), the lipase chain reaction (LCR), ribozyme-based methods, self sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), the use of Q Beta replicase, reverse transcription, nick translation, and the like.

Asymmetric amplification reactions may be used to preferentially amplify one strand representing the target sequence that is used for detection as the target polynucleotide. In some cases, the presence and/or amount of the amplification product itself may be used to determine the expression level of a given target sequence. In other instances, the amplification product may be used to hybridize to an array or other substrate comprising sensor polynucleotides which are used to detect and/or quantitate target sequence expression.

The first cycle of amplification in polymerase-based methods typically forms a primer extension product complementary to the template strand. If the template is single-stranded RNA, a polymerase with reverse transcriptase activity is used in the first amplification to reverse transcribe the RNA to DNA, and additional amplification cycles can be performed to copy the primer extension products. The primers for a PCR must, of course, be designed to hybridize to regions in their corresponding template that can produce an amplifiable segment; thus, each primer must hybridize so that its 3′ nucleotide is paired to a nucleotide in its complementary template strand that is located 3′ from the 3′ nucleotide of the primer used to replicate that complementary template strand in the PCR.

The target polynucleotide can be amplified by contacting one or more strands of the target polynucleotide with a primer and a polymerase having suitable activity to extend the primer and copy the target polynucleotide to produce a full-length complementary polynucleotide or a smaller portion thereof. Any enzyme having a polymerase activity that can copy the target polynucleotide can be used, including DNA polymerases, RNA polymerases, reverse transcriptases, enzymes having more than one type of polymerase or enzyme activity. The enzyme can be thermolabile or thermostable. Mixtures of enzymes can also be used. Exemplary enzymes include: DNA polymerases such as DNA Polymerase I (“Pol I”), the Klenow fragment of Pol I, T4, T7, Sequenase® T7, Sequenase® Version 2.0 T7, Tub, Taq, Tth, Pfic, Pfu, Tsp, Tfl, Tli and Pyrococcus sp GB-D DNA polymerases; RNA polymerases such as E. coli, SP6, T3 and T7 RNA polymerases; and reverse transcriptases such as AMV, M-MuLV, MMLV, RNAse H MMLV (SuperScript®), SuperScript® II, ThermoScript®, HIV-1, and RAV2 reverse transcriptases. All of these enzymes are commercially available. Exemplary polymerases with multiple specificities include RAV2 and Tli (exo-) polymerases. Exemplary thermostable polymerases include Tub, Taq, Tth, Pfic, Pfu, Tsp, Tfl, Tli and Pyrococcus sp. GB-D DNA polymerases.

Suitable reaction conditions are chosen to permit amplification of the target polynucleotide, including pH, buffer, ionic strength, presence and concentration of one or more salts, presence and concentration of reactants and cofactors such as nucleotides and magnesium and/or other metal ions (e.g., manganese), optional cosolvents, temperature, thermal cycling profile for amplification schemes comprising a polymerase chain reaction, and may depend in part on the polymerase being used as well as the nature of the sample. Cosolvents include formamide (typically at from about 2 to about 10%), glycerol (typically at from about 5 to about 10%), and DMSO (typically at from about 0.9 to about 10%). Techniques may be used in the amplification scheme in order to minimize the production of false positives or artifacts produced during amplification. These include “touchdown” PCR, hot-start techniques, use of nested primers, or designing PCR primers so that they form stem-loop structures in the event of primer-dimer formation and thus are not amplified. Techniques to accelerate PCR can be used, for example centrifugal PCR, which allows for greater convection within the sample, and comprising infrared heating steps for rapid heating and cooling of the sample. One or more cycles of amplification can be performed. An excess of one primer can be used to produce an excess of one primer extension product during PCR; preferably, the primer extension product produced in excess is the amplification product to be detected. A plurality of different primers may be used to amplify different target polynucleotides or different regions of a particular target polynucleotide within the sample.

An amplification reaction can be performed under conditions which allow an optionally labeled sensor polynucleotide to hybridize to the amplification product during at least part of an amplification cycle. When the assay is performed in this manner, real-time detection of this hybridization event can take place by monitoring for light emission or fluorescence during amplification, as known in the art.

Where the amplification product is to be used for hybridization to an array or microarray, a number of suitable commercially available amplification products are available. These include amplification kits available from NuGEN, Inc. (San Carlos, Calif.), including the WTA-Ovation™ System, WT-Ovation™ System v2, WT-Ovation™ Pico System, WT-Ovation™ FFPE Exon Module, WT-Ovation™ FFPE Exon Module RiboAmp and RiboAmp^(Plus) RNA Amplification Kits (MDS Analytical Technologies (formerly Arcturus) (Mountain View, Calif.), Genisphere, Inc. (Hatfield, Pa.), including the RampUp Plus™ and SenseAmp™ RNA Amplification kits, alone or in combination. Amplified nucleic acids may be subjected to one or more purification reactions after amplification and labeling, for example using magnetic beads (e.g., RNAC1ean magnetic beads, Agencourt Biosciences).

Multiple RNA biomarkers (e.g., RNA targets) can be analyzed using real-time quantitative multiplex RT-PCR platforms and other multiplexing technologies such as GenomeLab GeXP Genetic Analysis System (Beckman Coulter, Foster City, Calif.), SmartCycler® 9600 or GeneXpert® Systems (Cepheid, Sunnyvale, Calif.), ABI 7900 HT Fast Real Time PCR system (Applied Biosystems, Foster City, Calif.), LightCycler® 480 System (Roche Molecular Systems, Pleasanton, Calif.), xMAP 100 System (Luminex, Austin, Tex.) Solexa Genome Analysis System (Illumina, Hayward, Calif.), OpenArray Real Time qPCR (BioTrove, Woburn, Mass.) and BeadXpress System (Illumina, Hayward, Calif.). Alternatively, or additional, coding targets and/or non-coding targets can be analyzed using RNA-Seq. In some instances, coding and/or non-coding targets are analyzed by sequencing.

Detection and/or Quantification of Target Sequences

Any method of detecting and/or quantitating the expression of the encoded target sequences can in principle be used in the invention. The expressed target sequences can be directly detected and/or quantitated, or may be copied and/or amplified to allow detection of amplified copies of the expressed target sequences or its complement.

Methods for detecting and/or quantifying a target can include Northern blotting, sequencing, array or microarray hybridization, by enzymatic cleavage of specific structures (e.g., an Invader® assay, Third Wave Technologies, e.g. as described in U.S. Pat. Nos. 5,846,717, 6,090,543; 6,001,567; 5,985,557; and 5,994,069) and amplification methods (e.g. RT-PCR, including in a TaqMan® assay (PE Biosystems, Foster City, Calif., e.g. as described in U.S. Pat. Nos. 5,962,233 and 5,538,848)), and may be quantitative or semi-quantitative, and may vary depending on the origin, amount and condition of the available biological sample. Combinations of these methods may also be used. For example, nucleic acids may be amplified, labeled and subjected to microarray analysis.

In some instances, assaying the expression level of a plurality of targets comprises amplifying the plurality of targets. Amplifying the plurality of targets can comprise PCR, RT-PCR, qPCR, digital PCR, and nested PCR.

In some instances, the target sequences are detected by sequencing. Sequencing methods may comprise whole genome sequencing or exome sequencing. Sequencing methods such as Maxim-Gilbert, chain-termination, or high-throughput systems may also be used. Additional, suitable sequencing techniques include classic dideoxy sequencing reactions (Sanger method) using labeled terminators or primers and gel separation in slab or capillary, sequencing by synthesis using reversibly terminated labeled nucleotides, pyrosequencing, 454 sequencing, allele specific hybridization to a library of labeled oligonucleotide probes, sequencing by synthesis using allele specific hybridization to a library of labeled clones that is followed by ligation, real time monitoring of the incorporation of labeled nucleotides during a polymerization step, shotgun sequencing and SOLiD sequencing.

Additional methods for detecting and/or quantifying a target sequence can comprise single-molecule sequencing (e.g., Illumina, Helicos, PacBio, ABI SOLID), in situ hybridization, bead-array technologies (e.g., Luminex xMAP, Illumina BeadChips), branched DNA technology (e.g., Panomics, Genisphere), and Ion Torrent™.

In some instances, methods for detecting and/or quantifying a target sequence comprise transcriptome sequencing techniques. Transcription sequencing (e.g., RNA-seq, “Whole Transcriptome Shotgun Sequencing” (“WTSS”)) may comprise the use of high-throughput sequencing technologies to sequence cDNA in order to get information about a sample's RNA content. Transcriptome sequencing can provide information on differential expression of genes, including gene alleles and differently spliced transcripts, non-coding RNAs, post-transcriptional mutations or editing, and gene fusions. Transcriptomes can also be sequenced by methods comprising Sanger sequencing, Serial analysis of gene expression (SAGE), cap analysis gene expression (CAGE), and massively parallel signature sequencing (MPSS). In some instances, transcriptome sequencing can comprise a variety of platforms. A non-limiting list of exemplary platforms include an Illumina Genome Analyzer platform, ABI Solid Sequencing, and Life Science's 454 Sequencing.

Reverse Transcription for ORT-PCR Analysis

Reverse transcription can be performed by any method known in the art. For example, reverse transcription may be performed using the Omniscript kit (Qiagen, Valencia, Calif.), Superscript III kit (Invitrogen, Carlsbad, Calif.), for RT-PCR. Target-specific priming can be performed in order to increase the sensitivity of detection of target sequences and generate target-specific cDNA.

TaqMan® Gene Expression Analysis

TaqMan®RT-PCR can be performed using Applied Biosystems Prism (ABI) 7900 HT instruments in a 5 1.11 volume with target sequence-specific cDNA equivalent to 1 ng total RNA.

Primers and probes concentrations for TaqMan analysis are added to amplify fluorescent amplicons using PCR cycling conditions such as 95° C. for 10 minutes for one cycle, 95° C. for 20 seconds, and 60° C. for 45 seconds for 40 cycles. A reference sample can be assayed to ensure reagent and process stability. Negative controls (e.g., no template) should be assayed to monitor any exogenous nucleic acid contamination.

Classification Arrays

The present invention contemplates that a classifier, ICE block, PSR, probe set or probes derived therefrom may be provided in an array format. In the context of the present invention, an “array” is a spatially or logically organized collection of polynucleotide probes. An array comprising probes specific for a coding target, non-coding target, or a combination thereof may be used. Alternatively, an array comprising probes specific for two or more of transcripts listed in Table 6 or a product derived thereof can be used. Desirably, an array may be specific for at least about 5, 10, 15, 20, 25, 30, 50, 75, 100, 150, 200 or more of transcripts listed in Table 6. The array can be specific for at least about 250, 300, 350, 400 or more transcripts listed in Table 6. Expression of these sequences may be detected alone or in combination with other transcripts. In some embodiments, an array is used which comprises a wide range of sensor probes for prostate-specific expression products, along with appropriate control sequences. In some instances, the array may comprise the Human Exon 1.0 ST Array (HuEx 1.0 ST, Affymetrix, Inc., Santa Clara, Calif.).

Typically the polynucleotide probes are attached to a solid substrate and are ordered so that the location (on the substrate) and the identity of each are known. The polynucleotide probes can be attached to one of a variety of solid substrates capable of withstanding the reagents and conditions necessary for use of the array. Examples include, but are not limited to, polymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, polycarbonate, polypropylene and polystyrene; ceramic; silicon; silicon dioxide; modified silicon; (fused) silica, quartz or glass; functionalized glass; paper, such as filter paper; diazotized cellulose; nitrocellulose filter; nylon membrane; and polyacrylamide gel pad. Substrates that are transparent to light are useful for arrays that may be used in an assay that involves optical detection.

Examples of array formats include membrane or filter arrays (for example, nitrocellulose, nylon arrays), plate arrays (for example, multiwell, such as a 24-, 96-, 256-, 384-, 864- or 1536-well, microtitre plate arrays), pin arrays, and bead arrays (for example, in a liquid “slurry”). Arrays on substrates such as glass or ceramic slides are often referred to as chip arrays or “chips.” Such arrays are well known in the art. In one embodiment of the present invention, the Cancer Prognosticarray is a chip.

Annotation of Probe Selection Regions

In some instances, the methods disclosed herein comprise the annotation of one or more probe selection regions (PSRs). In some instances, the PSRs disclosed are annotated into categories (e.g., coding, non-coding). Annotation of the PSRs can utilize a variety of software packages. In some instances, annotation of the PSRs comprises the use of the xmapcore package (Yates et al 2010), which is the human genome version hg19, and Ensembl gene annotation v62, which can be integrated with the xmapcore packages. In some instances, the method for annotating a PSR comprises (a) annotating a PSR as Non_Coding (intronic), wherein the PSR is returned by the intronic( ) function; and/or (b) further analyzing a PSR, wherein the PSR is returned by the exonic( ) function. Further analysis of the PSR can comprise (a) annotating the PSR as Coding, wherein the PSR is returned by the coding.probesets( ) function; (b) annotating the PSR as Non_Coding (UTR), wherein the PSR is returned by the utr.probestes( ) function; and/or (c) annotating the PSR as Non_Coding (ncTRANSCRIPT), wherein the PSR is not annotated as Coding or NON_Coding (UTR). PSRs that are not annotated as Non_Coding (intronic), Non_Coding (UTR), Non_Coding (ncTRANSCRIPT), or Coding can be referred to as the remaining PSRs.

The methods disclosed herein can further comprise detailed annotation of the remaining PSRs. Detailed annotation of the remaining PSRs can comprise determining the chromosome, start position, end position, and strand for each remaining PSR. Detailed annotation of the remaining PSRs can comprise utilization of the probeset.to.hit( ) function. In some instances, the remaining PSRs can be further annotated. Further annotation of the remaining PSRs can comprise inspection of a genomic span of each remaining PSR for the presence of genes, exons and protein-coding sequences. Often, the opposite strand of the PSR is used in the inspection of the genomic span. In some instances, inspection of the genomic span can comprise the use of one or more computer functions. In some instances, the computer functions are a genes.in.range( ) function, exons.in.range( ) function, and/or proteins.in.range( ) function (respectively). The remaining PSRs can be annotated as (a) Non_Coding (CDS_Antisense), wherein a protein is returned for the proteins.in.range( ) function; (b) Non_Coding (UTR_Antisense), wherein (i) a protein is not returned for the proteins.in.range( ) function, and (ii) the overlapping feature of the gene in the opposite strand is a UTR; (c) Non_Coding (ncTRANSCRIPT_Antisense), wherein (i) a protein is not returned for the proteins.in.range( ) function, and (ii) the overlapping feature of the gene in the opposite strand is not a UTR; (d) Non_Coding (Intronic_Antisense), wherein (i) a gene is returned for the genes.in.range( ) function, (ii) an exon is not returned for the exons.in.range( ), and (iii) a protein is not returned for the proteins.in.range( ) function; and (e) Non_Coding (Intergenic), wherein the remaining PSR does not overlap with any coding or non-coding gene feature in the sense or antisense strand.

In some instances, the methods disclosed herein further comprise additional annotation of a PSR with respect to transcripts and genes. Additional annotation of the PSR can comprise the use of the probeset.to.transcript( ) and/or probeset.to.gene( ) functions. In some instances, PSRs are annotated as Non_Coding (Non_Unique), wherein the PSR is obtained using the unreliable( ) function from xmapcore. In some instances, a PSR is annotated as Non_Coding (Intergenic) when the PSR maps to more than one region.

Data Analysis

In some embodiments, one or more pattern recognition methods can be used in analyzing the expression level of target sequences. The pattern recognition method can comprise a linear combination of expression levels, or a nonlinear combination of expression levels. In some embodiments, expression measurements for RNA transcripts or combinations of RNA transcript levels are formulated into linear or non-linear models or algorithms (e.g., an ‘expression signature’) and converted into a likelihood score. This likelihood score can indicate the probability that a biological sample is from a patient who may exhibit no evidence of disease, who may exhibit local disease, who may exhibit systemic cancer, or who may exhibit biochemical recurrence. The likelihood score can be used to distinguish these disease states. The models and/or algorithms can be provided in machine readable format, and may be used to correlate expression levels or an expression profile with a disease state, and/or to designate a treatment modality for a patient or class of patients.

Assaying the expression level for a plurality of targets may comprise the use of an algorithm or classifier. Array data can be managed, classified, and analyzed using techniques known in the art. Assaying the expression level for a plurality of targets may comprise probe set modeling and data pre-processing. Probe set modeling and data pre-processing can be derived using the Robust Multi-Array (RMA) algorithm or variants GC-RMA, fRMA, Probe Logarithmic Intensity Error (PLIER) algorithm or variant iterPLIER. Variance or intensity filters can be applied to pre-process data using the RMA algorithm, for example by removing target sequences with a standard deviation of <10 or a mean intensity of <100 intensity units of a normalized data range, respectively.

Alternatively, assaying the expression level for a plurality of targets may comprise the use of a machine learning algorithm. The machine learning algorithm may comprise a supervised learning algorithm. Examples of supervised learning algorithms may include Average One-Dependence Estimators (AODE), Artificial neural network (e.g., Backpropagation), Bayesian statistics (e.g., Naive Bayes classifier, Bayesian network, Bayesian knowledge base), Case-based reasoning, Decision trees, Inductive logic programming, Gaussian process regression, Group method of data handling (GMDH), Learning Automata, Learning Vector Quantization, Minimum message length (decision trees, decision graphs, etc.), Lazy learning, Instance-based learning Nearest Neighbor Algorithm, Analogical modeling, Probably approximately correct learning (PAC) learning, Ripple down rules, a knowledge acquisition methodology, Symbolic machine learning algorithms, Subsymbolic machine learning algorithms, Support vector machines, Random Forests, Ensembles of classifiers, Bootstrap aggregating (bagging), and Boosting. Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN). Alternatively, supervised learning methods may comprise statistical classification, such as AODE, Linear classifiers (e.g., Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, and Support vector machine), quadratic classifiers, k-nearest neighbor, Boosting, Decision trees (e.g., C4.5, Random forests), Bayesian networks, and Hidden Markov models.

The machine learning algorithms may also comprise an unsupervised learning algorithm. Examples of unsupervised learning algorithms may include Artificial neural network, Data clustering, Expectation-maximization algorithm, Self-organizing map, Radial basis function network, Vector Quantization, Generative topographic map, Information bottleneck method, and IBSEAD. Unsupervised learning may also comprise association rule learning algorithms such as Apriori algorithm, Eclat algorithm and FP-growth algorithm. Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering, may also be used. Alternatively, unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering.

In some instances, the machine learning algorithms comprise a reinforcement learning algorithm. Examples of reinforcement learning algorithms include, but are not limited to, Temporal difference learning, Q-learning and Learning Automata. Alternatively, the machine learning algorithm may comprise Data Pre-processing.

Preferably, the machine learning algorithms may include, but are not limited to, Average One-Dependence Estimators (AODE), Fisher's linear discriminant, Logistic regression, Perceptron, Multilayer Perceptron, Artificial Neural Networks, Support vector machines, Quadratic classifiers, Boosting, Decision trees, C4.5, Bayesian networks, Hidden Markov models, High-Dimensional Discriminant Analysis, and Gaussian Mixture Models. The machine learning algorithm may comprise support vector machines, Naïve Bayes classifier, k-nearest neighbor, high-dimensional discriminant analysis, or Gaussian mixture models. In some instances, the machine learning algorithm comprises Random Forests.

The methods, systems, devices, and kits disclosed herein can further comprise a computer, an electronic device, computer software, a memory device, or any combination thereof. In some instances, the methods, systems, devices, and kits disclosed herein further comprise one or more computer software programs for (a) analysis of the target (e.g., expression profile, detection, quantification); (b) diagnosis, prognosis and/or monitoring the outcome or status of a cancer in a subject; (c) determination of a treatment regimen; (d) analysis of a classifier, probe set, probe selection region, ICE block, or digital Gleason score predictor as disclosed herein. Analysis of a classifier, probe set, probe selection region, ICE block or digital Gleason score predictor can comprise determining the AUC value, MDF value, percent accuracy, P-value, clinical significance, or any combination thereof. The software program can comprise (a) bigmemory, which can be used to load large expression matrices; (b) matrixStats, which can be used in statistics on matrices like row medians, column medians, row ranges; (c) genefilter, which can be used as a fast calculation of t-tests, ROC, and AUC; (d) pROC, which can be used to plot ROC curves and calculate AUC's and their 95% confidence intervals; (e) ROCR, which can be used to plot ROC curves and to calculate AUCs; (f) pROCR, which can be used to plot ROC curves and to calculate AUCs; (g) snow or doSMP, which can be used for parallel processing; (h) caret, which can be used for K-Nearest-Neighbour (KNN), Null Model, and classifier analysis; (i) e1071, which can be used for Support Vector Machines (SVM), K-Nearest-Neighbour (KNN), Naive Bayes, classifier tuning, and sample partitioning; (j) randomForest, which can be used for Random forest model; (k) HDClassif, which can be used for HDDA model; (l) rpart, which can be used for recursive partitioning model; (m) rms, which can be used for logistic regression model; (n) survival, which can be used for coxph model, km plots, and other survival analysis; (o) iterator, intertools, foreach, which can be used for iteration of large matrices; (p) frma, which can be used to package for frozen robust microarray analysis; (q) epitools, which can be used for odds ratios; (r) Proxy, which can be used for distance calculations; (s) boot, which can be used for Bootstrapping; (t) glmnet, which can be used to regularize general linear model; (u) gplots, which can be used to generate plots and figures; (v) scatterplot3d, which can be used to generate 3d scatter plots, (w) heatmap.plus, which can be used to generate heatmaps; (x) vegan, which can be used to determine MDS p-values; (y) xlsx, which can be used to work with excel spread sheets; (z) xtable, which can be used to work with R tables to latex; (aa) ffpe, which can be used for Cat plots; and (ab) xmapcore, which can be used for annotation of PSRs with respect to Ensembl annotation. In some instances, the software program is xmapcore. In other instances, the software program is caret. In other instances, the software program is e1071. The software program can be Proxy. Alternatively, the software program is gplots. In some instances, the software program is scatterplot3 d.

Additional Techniques and Tests

Factors known in the art for diagnosing and/or suggesting, selecting, designating, recommending or otherwise determining a course of treatment for a patient or class of patients suspected of having cancer can be employed in combination with measurements of the target sequence expression. The methods disclosed herein may include additional techniques such as cytology, histology, ultrasound analysis, MRI results, CT scan results, and measurements of PSA levels.

Certified tests for classifying disease status and/or designating treatment modalities may also be used in diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject. A certified test may comprise a means for characterizing the expression levels of one or more of the target sequences of interest, and a certification from a government regulatory agency endorsing use of the test for classifying the disease status of a biological sample.

In some embodiments, the certified test may comprise reagents for amplification reactions used to detect and/or quantitate expression of the target sequences to be characterized in the test. An array of probe nucleic acids can be used, with or without prior target amplification, for use in measuring target sequence expression.

The test is submitted to an agency having authority to certify the test for use in distinguishing disease status and/or outcome. Results of detection of expression levels of the target sequences used in the test and correlation with disease status and/or outcome are submitted to the agency. A certification authorizing the diagnostic and/or prognostic use of the test is obtained.

Also provided are portfolios of expression levels comprising a plurality of normalized expression levels of the target sequences described Table 6. Such portfolios may be provided by performing the methods described herein to obtain expression levels from an individual patient or from a group of patients. The expression levels can be normalized by any method known in the art; exemplary normalization methods that can be used in various embodiments include Robust Multichip Average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based and nonlinear normalization, and combinations thereof. Background correction can also be performed on the expression data; exemplary techniques useful for background correction include mode of intensities, normalized using median polish probe modeling and sketch-normalization.

In some embodiments, portfolios are established such that the combination of genes in the portfolio exhibit improved sensitivity and specificity relative to known methods. In considering a group of genes for inclusion in a portfolio, a small standard deviation in expression measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity. The invention also encompasses the above methods where the expression level determines the status or outcome of a cancer in the subject with at least about 45% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 50% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 55% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 60% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 65% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 70% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 75% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 80% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 85% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 90% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 95% specificity.

The invention also encompasses any of the methods disclosed herein where the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 45%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 50%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 55%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 60%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 65%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 70%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 75%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 80%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 85%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 90%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 95%.

The invention also encompasses the any of the methods disclosed herein where the sensitivity is at least about 45%. In some embodiments, the sensitivity is at least about 50%. In some embodiments, the sensitivity is at least about 55%. In some embodiments, the sensitivity is at least about 60%. In some embodiments, the sensitivity is at least about 65%. In some embodiments, the sensitivity is at least about 70%. In some embodiments, the sensitivity is at least about 75%. In some embodiments, the sensitivity is at least about 80%. In some embodiments, the sensitivity is at least about 85%. In some embodiments, the sensitivity is at least about 90%. In some embodiments, the sensitivity is at least about 95%.

In some instances, the methods disclosed herein may comprise the use of a genomic-clinical classifier (GCC) model. A general method for developing a GCC model may comprise (a) providing a sample from a subject suffering from a cancer; (b) assaying the expression level for a plurality of targets; (c) generating a model by using a machine learning algorithm. In some instances, the machine learning algorithm comprises Random Forests.

Cancer

The systems, compositions and methods disclosed herein may be used to diagnosis, monitor and/or predict the status or outcome of a cancer. Generally, a cancer is characterized by the uncontrolled growth of abnormal cells anywhere in a body. The abnormal cells may be termed cancer cells, malignant cells, or tumor cells. Many cancers and the abnormal cells that compose the cancer tissue are further identified by the name of the tissue that the abnormal cells originated from (for example, breast cancer, lung cancer, colon cancer, prostate cancer, pancreatic cancer, thyroid cancer). Cancer is not confined to humans; animals and other living organisms can get cancer.

In some instances, the cancer may be malignant. Alternatively, the cancer may be benign. The cancer may be a recurrent and/or refractory cancer. Most cancers can be classified as a carcinoma, sarcoma, leukemia, lymphoma, myeloma, or a central nervous system cancer.

The cancer may be a sarcoma. Sarcomas are cancers of the bone, cartilage, fat, muscle, blood vessels, or other connective or supportive tissue. Sarcomas include, but are not limited to, bone cancer, fibrosarcoma, chondrosarcoma, Ewing's sarcoma, malignant hemangioendothelioma, malignant schwannoma, bilateral vestibular schwannoma, osteosarcoma, soft tissue sarcomas (e.g. alveolar soft part sarcoma, angiosarcoma, cystosarcoma phylloides, dermatofibrosarcoma, desmoid tumor, epithelioid sarcoma, extraskeletal osteosarcoma, fibrosarcoma, hemangiopericytoma, hemangiosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant fibrous histiocytoma, neurofibrosarcoma, rhabdomyosarcoma, and synovial sarcoma).

Alternatively, the cancer may be a carcinoma. Carcinomas are cancers that begin in the epithelial cells, which are cells that cover the surface of the body, produce hormones, and make up glands. By way of non-limiting example, carcinomas include breast cancer, pancreatic cancer, lung cancer, colon cancer, colorectal cancer, rectal cancer, kidney cancer, bladder cancer, stomach cancer, prostate cancer, liver cancer, ovarian cancer, brain cancer, vaginal cancer, vulvar cancer, uterine cancer, oral cancer, penic cancer, testicular cancer, esophageal cancer, skin cancer, cancer of the fallopian tubes, head and neck cancer, gastrointestinal stromal cancer, adenocarcinoma, cutaneous or intraocular melanoma, cancer of the anal region, cancer of the small intestine, cancer of the endocrine system, cancer of the thyroid gland, cancer of the parathyroid gland, cancer of the adrenal gland, cancer of the urethra, cancer of the renal pelvis, cancer of the ureter, cancer of the endometrium, cancer of the cervix, cancer of the pituitary gland, neoplasms of the central nervous system (CNS), primary CNS lymphoma, brain stem glioma, and spinal axis tumors. In some instances, the cancer is a skin cancer, such as a basal cell carcinoma, squamous, melanoma, nonmelanoma, or actinic (solar) keratosis. Preferably, the cancer is a prostate cancer. Alternatively, the cancer may be a thyroid cancer. The cancer can be a pancreatic cancer. In some instances, the cancer is a bladder cancer.

In some instances, the cancer is a lung cancer. Lung cancer can start in the airways that branch off the trachea to supply the lungs (bronchi) or the small air sacs of the lung (the alveoli). Lung cancers include non-small cell lung carcinoma (NSCLC), small cell lung carcinoma, and mesotheliomia. Examples of NSCLC include squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. The mesothelioma may be a cancerous tumor of the lining of the lung and chest cavity (pleura) or lining of the abdomen (peritoneum). The mesothelioma may be due to asbestos exposure. The cancer may be a brain cancer, such as a glioblastoma.

Alternatively, the cancer may be a central nervous system (CNS) tumor. CNS tumors may be classified as gliomas or nongliomas. The glioma may be malignant glioma, high grade glioma, diffuse intrinsic pontine glioma. Examples of gliomas include astrocytomas, oligodendrogliomas (or mixtures of oligodendroglioma and astocytoma elements), and ependymomas. Astrocytomas include, but are not limited to, low-grade astrocytomas, anaplastic astrocytomas, glioblastoma multiforme, pilocytic astrocytoma, pleomorphic xanthoastrocytoma, and subependymal giant cell astrocytoma. Oligodendrogliomas include low-grade oligodendrogliomas (or oligoastrocytomas) and anaplastic oligodendriogliomas. Nongliomas include meningiomas, pituitary adenomas, primary CNS lymphomas, and medulloblastomas. In some instances, the cancer is a meningioma.

The cancer may be leukemia. The leukemia may be an acute lymphocytic leukemia, acute myelocytic leukemia, chronic lymphocytic leukemia, or chronic myelocytic leukemia. Additional types of leukemias include hairy cell leukemia, chronic myelomonocytic leukemia, and juvenile myelomonocytic-leukemia.

In some instances, the cancer is a lymphoma. Lymphomas are cancers of the lymphocytes and may develop from either B or T lymphocytes. The two major types of lymphoma are Hodgkin's lymphoma, previously known as Hodgkin's disease, and non-Hodgkin's lymphoma. Hodgkin's lymphoma is marked by the presence of the Reed-Sternberg cell. Non-Hodgkin's lymphomas are all lymphomas which are not Hodgkin's lymphoma. Non-Hodgkin lymphomas may be indolent lymphomas and aggressive lymphomas. Non-Hodgkin's lymphomas include, but are not limited to, diffuse large B cell lymphoma, follicular lymphoma, mucosa-associated lymphatic tissue lymphoma (MALT), small cell lymphocytic lymphoma, mantle cell lymphoma, Burkitt's lymphoma, mediastinal large B cell lymphoma, Waldenström macroglobulinemia, nodal marginal zone B cell lymphoma (NMZL), splenic marginal zone lymphoma (SMZL), extranodal marginal zone B cell lymphoma, intravascular large B cell lymphoma, primary effusion lymphoma, and lymphomatoid granulomatosis.

Cancer Staging

Diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise determining the stage of the cancer. Generally, the stage of a cancer is a description (usually numbers I to IV with IV having more progression) of the extent the cancer has spread. The stage often takes into account the size of a tumor, how deeply it has penetrated, whether it has invaded adjacent organs, how many lymph nodes it has metastasized to (if any), and whether it has spread to distant organs. Staging of cancer can be used as a predictor of survival, and cancer treatment may be determined by staging. Determining the stage of the cancer may occur before, during, or after treatment. The stage of the cancer may also be determined at the time of diagnosis.

Cancer staging can be divided into a clinical stage and a pathologic stage. Cancer staging may comprise the TNM classification. Generally, the TNM Classification of Malignant Tumours (TNM) is a cancer staging system that describes the extent of cancer in a patient's body. T may describe the size of the tumor and whether it has invaded nearby tissue, N may describe regional lymph nodes that are involved, and M may describe distant metastasis (spread of cancer from one body part to another). In the TNM (Tumor, Node, Metastasis) system, clinical stage and pathologic stage are denoted by a small “c” or “p” before the stage (e.g., cT3N1M0 or pT2N0).

Often, clinical stage and pathologic stage may differ. Clinical stage may be based on all of the available information obtained before a surgery to remove the tumor. Thus, it may include information about the tumor obtained by physical examination, radiologic examination, and endoscopy. Pathologic stage can add additional information gained by examination of the tumor microscopically by a pathologist. Pathologic staging can allow direct examination of the tumor and its spread, contrasted with clinical staging which may be limited by the fact that the information is obtained by making indirect observations at a tumor which is still in the body. The TNM staging system can be used for most forms of cancer.

Alternatively, staging may comprise Ann Arbor staging. Generally, Ann Arbor staging is the staging system for lymphomas, both in Hodgkin's lymphoma (previously called Hodgkin's disease) and Non-Hodgkin lymphoma (abbreviated NHL). The stage may depend on both the place where the malignant tissue is located (as located with biopsy, CT scanning and increasingly positron emission tomography) and on systemic symptoms due to the lymphoma (“B symptoms”: night sweats, weight loss of >10% or fevers). The principal stage may be determined by location of the tumor. Stage I may indicate that the cancer is located in a single region, usually one lymph node and the surrounding area. Stage I often may not have outward symptoms. Stage II can indicate that the cancer is located in two separate regions, an affected lymph node or organ and a second affected area, and that both affected areas are confined to one side of the diaphragm—that is, both are above the diaphragm, or both are below the diaphragm. Stage III often indicates that the cancer has spread to both sides of the diaphragm, including one organ or area near the lymph nodes or the spleen. Stage IV may indicate diffuse or disseminated involvement of one or more extralymphatic organs, including any involvement of the liver, bone marrow, or nodular involvement of the lungs.

Modifiers may also be appended to some stages. For example, the letters A, B, E, X, or S can be appended to some stages. Generally, A or B may indicate the absence of constitutional (B-type) symptoms is denoted by adding an “A” to the stage; the presence is denoted by adding a “B” to the stage. E can be used if the disease is “extranodal” (not in the lymph nodes) or has spread from lymph nodes to adjacent tissue. X is often used if the largest deposit is >10 cm large (“bulky disease”), or whether the mediastinum is wider than ⅓ of the chest on a chest X-ray. S may be used if the disease has spread to the spleen.

The nature of the staging may be expressed with CS or PS. CS may denote that the clinical stage as obtained by doctor's examinations and tests. PS may denote that the pathological stage as obtained by exploratory laparotomy (surgery performed through an abdominal incision) with splenectomy (surgical removal of the spleen).

Therapeutic Regimens

Diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise treating a cancer or preventing a cancer progression. In addition, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise identifying or predicting responders to an anti-cancer therapy. In some instances, diagnosing, predicting, or monitoring may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapy. Alternatively, determining a therapeutic regimen may comprise modifying, recommending, continuing or discontinuing an anti-cancer regimen. In some instances, if the sample expression patterns are consistent with the expression pattern for a known disease or disease outcome, the expression patterns can be used to designate one or more treatment modalities (e.g., therapeutic regimens, anti-cancer regimen). An anti-cancer regimen may comprise one or more anti-cancer therapies. Examples of anti-cancer therapies include surgery, chemotherapy, radiation therapy, immunotherapy/biological therapy, photodynamic therapy.

Surgical oncology uses surgical methods to diagnose, stage, and treat cancer, and to relieve certain cancer-related symptoms. Surgery may be used to remove the tumor (e.g., excisions, resections, debulking surgery), reconstruct a part of the body (e.g., restorative surgery), and/or to relieve symptoms such as pain (e.g., palliative surgery). Surgery may also include cryosurgery. Cryosurgery (also called cryotherapy) may use extreme cold produced by liquid nitrogen (or argon gas) to destroy abnormal tissue. Cryosurgery can be used to treat external tumors, such as those on the skin. For external tumors, liquid nitrogen can be applied directly to the cancer cells with a cotton swab or spraying device. Cryosurgery may also be used to treat tumors inside the body (internal tumors and tumors in the bone). For internal tumors, liquid nitrogen or argon gas may be circulated through a hollow instrument called a cryoprobe, which is placed in contact with the tumor. An ultrasound or MRI may be used to guide the cryoprobe and monitor the freezing of the cells, thus limiting damage to nearby healthy tissue. A ball of ice crystals may form around the probe, freezing nearby cells. Sometimes more than one probe is used to deliver the liquid nitrogen to various parts of the tumor. The probes may be put into the tumor during surgery or through the skin (percutaneously). After cryosurgery, the frozen tissue thaws and may be naturally absorbed by the body (for internal tumors), or may dissolve and form a scab (for external tumors).

Chemotherapeutic agents may also be used for the treatment of cancer. Examples of chemotherapeutic agents include alkylating agents, anti-metabolites, plant alkaloids and terpenoids, vinca alkaloids, podophyllotoxin, taxanes, topoisomerase inhibitors, and cytotoxic antibiotics. Cisplatin, carboplatin, and oxaliplatin are examples of alkylating agents. Other alkylating agents include mechlorethamine, cyclophosphamide, chlorambucil, ifosfamide. Alkylating agents may impair cell function by forming covalent bonds with the amino, carboxyl, sulfhydryl, and phosphate groups in biologically important molecules. Alternatively, alkylating agents may chemically modify a cell's DNA.

Anti-metabolites are another example of chemotherapeutic agents. Anti-metabolites may masquerade as purines or pyrimidines and may prevent purines and pyrimidines from becoming incorporated in to DNA during the “S” phase (of the cell cycle), thereby stopping normal development and division. Antimetabolites may also affect RNA synthesis. Examples of metabolites include azathioprine and mercaptopurine.

Alkaloids may be derived from plants and block cell division may also be used for the treatment of cancer. Alkyloids may prevent microtubule function. Examples of alkaloids are vinca alkaloids and taxanes. Vinca alkaloids may bind to specific sites on tubulin and inhibit the assembly of tubulin into microtubules (M phase of the cell cycle). The vinca alkaloids may be derived from the Madagascar periwinkle, Catharanthus roseus (formerly known as Vinca rosea). Examples of vinca alkaloids include, but are not limited to, vincristine, vinblastine, vinorelbine, or vindesine. Taxanes are diterpenes produced by the plants of the genus Taxus (yews). Taxanes may be derived from natural sources or synthesized artificially. Taxanes include paclitaxel (Taxol) and docetaxel (Taxotere). Taxanes may disrupt microtubule function. Microtubules are essential to cell division, and taxanes may stabilize GDP-bound tubulin in the microtubule, thereby inhibiting the process of cell division. Thus, in essence, taxanes may be mitotic inhibitors. Taxanes may also be radiosensitizing and often contain numerous chiral centers.

Alternative chemotherapeutic agents include podophyllotoxin. Podophyllotoxin is a plant-derived compound that may help with digestion and may be used to produce cytostatic drugs such as etoposide and teniposide. They may prevent the cell from entering the G1 phase (the start of DNA replication) and the replication of DNA (the S phase).

Topoisomerases are essential enzymes that maintain the topology of DNA Inhibition of type I or type II topoisomerases may interfere with both transcription and replication of DNA by upsetting proper DNA supercoiling. Some chemotherapeutic agents may inhibit topoisomerases. For example, some type I topoisomerase inhibitors include camptothecins: irinotecan and topotecan. Examples of type II inhibitors include amsacrine, etoposide, etoposide phosphate, and teniposide.

Another example of chemotherapeutic agents is cytotoxic antibiotics. Cytotoxic antibiotics are a group of antibiotics that are used for the treatment of cancer because they may interfere with DNA replication and/or protein synthesis. Cytotoxic antibiotics include, but are not limited to, actinomycin, anthracyclines, doxorubicin, daunorubicin, valrubicin, idarubicin, epirubicin, bleomycin, plicamycin, and mitomycin.

In some instances, the anti-cancer treatment may comprise radiation therapy. Radiation can come from a machine outside the body (external-beam radiation therapy) or from radioactive material placed in the body near cancer cells (internal radiation therapy, more commonly called brachytherapy). Systemic radiation therapy uses a radioactive substance, given by mouth or into a vein that travels in the blood to tissues throughout the body.

External-beam radiation therapy may be delivered in the form of photon beams (either x-rays or gamma rays). A photon is the basic unit of light and other forms of electromagnetic radiation. An example of external-beam radiation therapy is called 3-dimensional conformal radiation therapy (3D-CRT). 3D-CRT may use computer software and advanced treatment machines to deliver radiation to very precisely shaped target areas. Many other methods of external-beam radiation therapy are currently being tested and used in cancer treatment. These methods include, but are not limited to, intensity-modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT), Stereotactic radiosurgery (SRS), Stereotactic body radiation therapy (SBRT), and proton therapy.

Intensity-modulated radiation therapy (IMRT) is an example of external-beam radiation and may use hundreds of tiny radiation beam-shaping devices, called collimators, to deliver a single dose of radiation. The collimators can be stationary or can move during treatment, allowing the intensity of the radiation beams to change during treatment sessions. This kind of dose modulation allows different areas of a tumor or nearby tissues to receive different doses of radiation. IMRT is planned in reverse (called inverse treatment planning). In inverse treatment planning, the radiation doses to different areas of the tumor and surrounding tissue are planned in advance, and then a high-powered computer program calculates the required number of beams and angles of the radiation treatment. In contrast, during traditional (forward) treatment planning, the number and angles of the radiation beams are chosen in advance and computers calculate how much dose may be delivered from each of the planned beams. The goal of IMRT is to increase the radiation dose to the areas that need it and reduce radiation exposure to specific sensitive areas of surrounding normal tissue.

Another example of external-beam radiation is image-guided radiation therapy (IGRT). In IGRT, repeated imaging scans (CT, MRI, or PET) may be performed during treatment. These imaging scans may be processed by computers to identify changes in a tumor's size and location due to treatment and to allow the position of the patient or the planned radiation dose to be adjusted during treatment as needed. Repeated imaging can increase the accuracy of radiation treatment and may allow reductions in the planned volume of tissue to be treated, thereby decreasing the total radiation dose to normal tissue.

Tomotherapy is a type of image-guided IMRT. A tomotherapy machine is a hybrid between a CT imaging scanner and an external-beam radiation therapy machine. The part of the tomotherapy machine that delivers radiation for both imaging and treatment can rotate completely around the patient in the same manner as a normal CT scanner. Tomotherapy machines can capture CT images of the patient's tumor immediately before treatment sessions, to allow for very precise tumor targeting and sparing of normal tissue.

Stereotactic radiosurgery (SRS) can deliver one or more high doses of radiation to a small tumor. SRS uses extremely accurate image-guided tumor targeting and patient positioning. Therefore, a high dose of radiation can be given without excess damage to normal tissue. SRS can be used to treat small tumors with well-defined edges. It is most commonly used in the treatment of brain or spinal tumors and brain metastases from other cancer types. For the treatment of some brain metastases, patients may receive radiation therapy to the entire brain (called whole-brain radiation therapy) in addition to SRS. SRS requires the use of a head frame or other device to immobilize the patient during treatment to ensure that the high dose of radiation is delivered accurately.

Stereotactic body radiation therapy (SBRT) delivers radiation therapy in fewer sessions, using smaller radiation fields and higher doses than 3D-CRT in most cases. SBRT may treat tumors that lie outside the brain and spinal cord. Because these tumors are more likely to move with the normal motion of the body, and therefore cannot be targeted as accurately as tumors within the brain or spine, SBRT is usually given in more than one dose. SBRT can be used to treat small, isolated tumors, including cancers in the lung and liver. SBRT systems may be known by their brand names, such as the CyberKnife®.

In proton therapy, external-beam radiation therapy may be delivered by proton. Protons are a type of charged particle. Proton beams differ from photon beams mainly in the way they deposit energy in living tissue. Whereas photons deposit energy in small packets all along their path through tissue, protons deposit much of their energy at the end of their path (called the Bragg peak) and deposit less energy along the way. Use of protons may reduce the exposure of normal tissue to radiation, possibly allowing the delivery of higher doses of radiation to a tumor.

Other charged particle beams such as electron beams may be used to irradiate superficial tumors, such as skin cancer or tumors near the surface of the body, but they cannot travel very far through tissue.

Internal radiation therapy (brachytherapy) is radiation delivered from radiation sources (radioactive materials) placed inside or on the body. Several brachytherapy techniques are used in cancer treatment. Interstitial brachytherapy may use a radiation source placed within tumor tissue, such as within a prostate tumor. Intracavitary brachytherapy may use a source placed within a surgical cavity or a body cavity, such as the chest cavity, near a tumor. Episcleral brachytherapy, which may be used to treat melanoma inside the eye, may use a source that is attached to the eye. In brachytherapy, radioactive isotopes can be sealed in tiny pellets or “seeds.” These seeds may be placed in patients using delivery devices, such as needles, catheters, or some other type of carrier. As the isotopes decay naturally, they give off radiation that may damage nearby cancer cells. Brachytherapy may be able to deliver higher doses of radiation to some cancers than external-beam radiation therapy while causing less damage to normal tissue.

Brachytherapy can be given as a low-dose-rate or a high-dose-rate treatment. In low-dose-rate treatment, cancer cells receive continuous low-dose radiation from the source over a period of several days. In high-dose-rate treatment, a robotic machine attached to delivery tubes placed inside the body may guide one or more radioactive sources into or near a tumor, and then removes the sources at the end of each treatment session. High-dose-rate treatment can be given in one or more treatment sessions. An example of a high-dose-rate treatment is the MammoSite® system. Bracytherapy may be used to treat patients with breast cancer who have undergone breast-conserving surgery.

The placement of brachytherapy sources can be temporary or permanent. For permanent brachytherapy, the sources may be surgically sealed within the body and left there, even after all of the radiation has been given off. In some instances, the remaining material (in which the radioactive isotopes were sealed) does not cause any discomfort or harm to the patient. Permanent brachytherapy is a type of low-dose-rate brachytherapy. For temporary brachytherapy, tubes (catheters) or other carriers are used to deliver the radiation sources, and both the carriers and the radiation sources are removed after treatment. Temporary brachytherapy can be either low-dose-rate or high-dose-rate treatment. Brachytherapy may be used alone or in addition to external-beam radiation therapy to provide a “boost” of radiation to a tumor while sparing surrounding normal tissue.

In systemic radiation therapy, a patient may swallow or receive an injection of a radioactive substance, such as radioactive iodine or a radioactive substance bound to a monoclonal antibody. Radioactive iodine (131I) is a type of systemic radiation therapy commonly used to help treat cancer, such as thyroid cancer. Thyroid cells naturally take up radioactive iodine. For systemic radiation therapy for some other types of cancer, a monoclonal antibody may help target the radioactive substance to the right place. The antibody joined to the radioactive substance travels through the blood, locating and killing tumor cells. For example, the drug ibritumomab tiuxetan (Zevalin®) may be used for the treatment of certain types of B-cell non-Hodgkin lymphoma (NHL). The antibody part of this drug recognizes and binds to a protein found on the surface of B lymphocytes. The combination drug regimen of tositumomab and iodine I 131 tositumomab (Bexxar®) may be used for the treatment of certain types of cancer, such as NHL. In this regimen, nonradioactive tositumomab antibodies may be given to patients first, followed by treatment with tositumomab antibodies that have 131I attached. Tositumomab may recognize and bind to the same protein on B lymphocytes as ibritumomab. The nonradioactive form of the antibody may help protect normal B lymphocytes from being damaged by radiation from 131I.

Some systemic radiation therapy drugs relieve pain from cancer that has spread to the bone (bone metastases). This is a type of palliative radiation therapy. The radioactive drugs samarium-153-lexidronam (Quadramet®) and strontium-89 chloride (Metastron®) are examples of radiopharmaceuticals may be used to treat pain from bone metastases.

Biological therapy (sometimes called immunotherapy, biotherapy, or biological response modifier (BRM) therapy) uses the body's immune system, either directly or indirectly, to fight cancer or to lessen the side effects that may be caused by some cancer treatments. Biological therapies include interferons, interleukins, colony-stimulating factors, monoclonal antibodies, vaccines, gene therapy, and nonspecific immunomodulating agents.

Interferons (IFNs) are types of cytokines that occur naturally in the body. Interferon alpha, interferon beta, and interferon gamma are examples of interferons that may be used in cancer treatment.

Like interferons, interleukins (ILs) are cytokines that occur naturally in the body and can be made in the laboratory. Many interleukins have been identified for the treatment of cancer. For example, interleukin-2 (IL-2 or aldesleukin), interleukin 7, and interleukin 12 have may be used as an anti-cancer treatment. IL-2 may stimulate the growth and activity of many immune cells, such as lymphocytes, that can destroy cancer cells. Interleukins may be used to treat a number of cancers, including leukemia, lymphoma, and brain, colorectal, ovarian, breast, kidney and prostate cancers.

Colony-stimulating factors (CSFs) (sometimes called hematopoietic growth factors) may also be used for the treatment of cancer. Some examples of CSFs include, but are not limited to, G-CSF (filgrastim) and GM-CSF (sargramostim). CSFs may promote the division of bone marrow stem cells and their development into white blood cells, platelets, and red blood cells. Bone marrow is critical to the body's immune system because it is the source of all blood cells. Because anticancer drugs can damage the body's ability to make white blood cells, red blood cells, and platelets, stimulation of the immune system by CSFs may benefit patients undergoing other anti-cancer treatment, thus CSFs may be combined with other anti-cancer therapies, such as chemotherapy. CSFs may be used to treat a large variety of cancers, including lymphoma, leukemia, multiple myeloma, melanoma, and cancers of the brain, lung, esophagus, breast, uterus, ovary, prostate, kidney, colon, and rectum.

Another type of biological therapy includes monoclonal antibodies (MOABs or MoABs). These antibodies may be produced by a single type of cell and may be specific for a particular antigen. To create MOABs, human cancer cells may be injected into mice. In response, the mouse immune system can make antibodies against these cancer cells. The mouse plasma cells that produce antibodies may be isolated and fused with laboratory-grown cells to create “hybrid” cells called hybridomas. Hybridomas can indefinitely produce large quantities of these pure antibodies, or MOABs. MOABs may be used in cancer treatment in a number of ways. For instance, MOABs that react with specific types of cancer may enhance a patient's immune response to the cancer. MOABs can be programmed to act against cell growth factors, thus interfering with the growth of cancer cells.

MOABs may be linked to other anti-cancer therapies such as chemotherapeutics, radioisotopes (radioactive substances), other biological therapies, or other toxins. When the antibodies latch onto cancer cells, they deliver these anti-cancer therapies directly to the tumor, helping to destroy it. MOABs carrying radioisotopes may also prove useful in diagnosing certain cancers, such as colorectal, ovarian, and prostate.

Rituxan® (rituximab) and Herceptin® (trastuzumab) are examples of MOABs that may be used as a biological therapy. Rituxan may be used for the treatment of non-Hodgkin lymphoma. Herceptin can be used to treat metastatic breast cancer in patients with tumors that produce excess amounts of a protein called HER2. Alternatively, MOABs may be used to treat lymphoma, leukemia, melanoma, and cancers of the brain, breast, lung, kidney, colon, rectum, ovary, prostate, and other areas.

Cancer vaccines are another form of biological therapy. Cancer vaccines may be designed to encourage the patient's immune system to recognize cancer cells. Cancer vaccines may be designed to treat existing cancers (therapeutic vaccines) or to prevent the development of cancer (prophylactic vaccines). Therapeutic vaccines may be injected in a person after cancer is diagnosed. These vaccines may stop the growth of existing tumors, prevent cancer from recurring, or eliminate cancer cells not killed by prior treatments. Cancer vaccines given when the tumor is small may be able to eradicate the cancer. On the other hand, prophylactic vaccines are given to healthy individuals before cancer develops. These vaccines are designed to stimulate the immune system to attack viruses that can cause cancer. By targeting these cancer-causing viruses, development of certain cancers may be prevented. For example, cervarix and gardasil are vaccines to treat human papilloma virus and may prevent cervical cancer. Therapeutic vaccines may be used to treat melanoma, lymphoma, leukemia, and cancers of the brain, breast, lung, kidney, ovary, prostate, pancreas, colon, and rectum. Cancer vaccines can be used in combination with other anti-cancer therapies.

Gene therapy is another example of a biological therapy. Gene therapy may involve introducing genetic material into a person's cells to fight disease. Gene therapy methods may improve a patient's immune response to cancer. For example, a gene may be inserted into an immune cell to enhance its ability to recognize and attack cancer cells. In another approach, cancer cells may be injected with genes that cause the cancer cells to produce cytokines and stimulate the immune system.

In some instances, biological therapy includes nonspecific immunomodulating agents. Nonspecific immunomodulating agents are substances that stimulate or indirectly augment the immune system. Often, these agents target key immune system cells and may cause secondary responses such as increased production of cytokines and immunoglobulins. Two nonspecific immunomodulating agents used in cancer treatment are bacillus Calmette-Guerin (BCG) and levamisole. BCG may be used in the treatment of superficial bladder cancer following surgery. BCG may work by stimulating an inflammatory, and possibly an immune, response. A solution of BCG may be instilled in the bladder. Levamisole is sometimes used along with fluorouracil (5-FU) chemotherapy in the treatment of stage III (Dukes' C) colon cancer following surgery. Levamisole may act to restore depressed immune function.

Photodynamic therapy (PDT) is an anti-cancer treatment that may use a drug, called a photosensitizer or photosensitizing agent, and a particular type of light. When photosensitizers are exposed to a specific wavelength of light, they may produce a form of oxygen that kills nearby cells. A photosensitizer may be activated by light of a specific wavelength. This wavelength determines how far the light can travel into the body. Thus, photosensitizers and wavelengths of light may be used to treat different areas of the body with PDT.

In the first step of PDT for cancer treatment, a photosensitizing agent may be injected into the bloodstream. The agent may be absorbed by cells all over the body but may stay in cancer cells longer than it does in normal cells. Approximately 24 to 72 hours after injection, when most of the agent has left normal cells but remains in cancer cells, the tumor can be exposed to light. The photosensitizer in the tumor can absorb the light and produces an active form of oxygen that destroys nearby cancer cells. In addition to directly killing cancer cells, PDT may shrink or destroy tumors in two other ways. The photosensitizer can damage blood vessels in the tumor, thereby preventing the cancer from receiving necessary nutrients. PDT may also activate the immune system to attack the tumor cells.

The light used for PDT can come from a laser or other sources. Laser light can be directed through fiber optic cables (thin fibers that transmit light) to deliver light to areas inside the body. For example, a fiber optic cable can be inserted through an endoscope (a thin, lighted tube used to look at tissues inside the body) into the lungs or esophagus to treat cancer in these organs. Other light sources include light-emitting diodes (LEDs), which may be used for surface tumors, such as skin cancer. PDT is usually performed as an outpatient procedure. PDT may also be repeated and may be used with other therapies, such as surgery, radiation, or chemotherapy.

Extracorporeal photopheresis (ECP) is a type of PDT in which a machine may be used to collect the patient's blood cells. The patient's blood cells may be treated outside the body with a photosensitizing agent, exposed to light, and then returned to the patient. ECP may be used to help lessen the severity of skin symptoms of cutaneous T-cell lymphoma that has not responded to other therapies. ECP may be used to treat other blood cancers, and may also help reduce rejection after transplants.

Additionally, photosensitizing agent, such as porfimer sodium or Photofrin®, may be used in PDT to treat or relieve the symptoms of esophageal cancer and non-small cell lung cancer. Porfimer sodium may relieve symptoms of esophageal cancer when the cancer obstructs the esophagus or when the cancer cannot be satisfactorily treated with laser therapy alone. Porfimer sodium may be used to treat non-small cell lung cancer in patients for whom the usual treatments are not appropriate, and to relieve symptoms in patients with non-small cell lung cancer that obstructs the airways. Porfimer sodium may also be used for the treatment of precancerous lesions in patients with Barrett esophagus, a condition that can lead to esophageal cancer.

Laser therapy may use high-intensity light to treat cancer and other illnesses. Lasers can be used to shrink or destroy tumors or precancerous growths. Lasers are most commonly used to treat superficial cancers (cancers on the surface of the body or the lining of internal organs) such as basal cell skin cancer and the very early stages of some cancers, such as cervical, penile, vaginal, vulvar, and non-small cell lung cancer.

Lasers may also be used to relieve certain symptoms of cancer, such as bleeding or obstruction. For example, lasers can be used to shrink or destroy a tumor that is blocking a patient's trachea (windpipe) or esophagus. Lasers also can be used to remove colon polyps or tumors that are blocking the colon or stomach.

Laser therapy is often given through a flexible endoscope (a thin, lighted tube used to look at tissues inside the body). The endoscope is fitted with optical fibers (thin fibers that transmit light). It is inserted through an opening in the body, such as the mouth, nose, anus, or vagina. Laser light is then precisely aimed to cut or destroy a tumor.

Laser-induced interstitial thermotherapy (LITT), or interstitial laser photocoagulation, also uses lasers to treat some cancers. LITT is similar to a cancer treatment called hyperthermia, which uses heat to shrink tumors by damaging or killing cancer cells. During LITT, an optical fiber is inserted into a tumor. Laser light at the tip of the fiber raises the temperature of the tumor cells and damages or destroys them. LITT is sometimes used to shrink tumors in the liver.

Laser therapy can be used alone, but most often it is combined with other treatments, such as surgery, chemotherapy, or radiation therapy. In addition, lasers can seal nerve endings to reduce pain after surgery and seal lymph vessels to reduce swelling and limit the spread of tumor cells.

Lasers used to treat cancer may include carbon dioxide (CO2) lasers, argon lasers, and neodymium:yttrium-aluminum-garnet (Nd:YAG) lasers. Each of these can shrink or destroy tumors and can be used with endoscopes. CO2 and argon lasers can cut the skin's surface without going into deeper layers. Thus, they can be used to remove superficial cancers, such as skin cancer. In contrast, the Nd:YAG laser is more commonly applied through an endoscope to treat internal organs, such as the uterus, esophagus, and colon. Nd:YAG laser light can also travel through optical fibers into specific areas of the body during LITT. Argon lasers are often used to activate the drugs used in PDT.

For patients with high test scores consistent with systemic disease outcome after prostatectomy, additional treatment modalities such as adjuvant chemotherapy (e.g., docetaxel, mitoxantrone and prednisone), systemic radiation therapy (e.g., samarium or strontium) and/or anti-androgen therapy (e.g., surgical castration, finasteride, dutasteride) can be designated. Such patients would likely be treated immediately with anti-androgen therapy alone or in combination with radiation therapy in order to eliminate presumed micro-metastatic disease, which cannot be detected clinically but can be revealed by the target sequence expression signature.

Such patients can also be more closely monitored for signs of disease progression. For patients with intermediate test scores consistent with biochemical recurrence only (BCR-only or elevated PSA that does not rapidly become manifested as systemic disease only localized adjuvant therapy (e.g., radiation therapy of the prostate bed) or short course of anti-androgen therapy would likely be administered. Patients with scores consistent with metastasis or disease progression would likely be administered increased dosage of an anti-cancer therapy and/or administered an adjuvant therapy. For patients with low scores or scores consistent with no evidence of disease (NED) or no disease progression, adjuvant therapy would not likely be recommended by their physicians in order to avoid treatment-related side effects such as metabolic syndrome (e.g., hypertension, diabetes and/or weight gain), osteoporosis, proctitis, incontinence or impotence. Patients with samples consistent with NED or no disease progression could be designated for watchful waiting, or for no treatment. Patients with test scores that do not correlate with systemic disease but who have successive PSA increases could be designated for watchful waiting, increased monitoring, or lower dose or shorter duration anti-androgen therapy.

Target sequences can be grouped so that information obtained about the set of target sequences in the group can be used to make or assist in making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice.

A patient report is also provided comprising a representation of measured expression levels of a plurality of target sequences in a biological sample from the patient, wherein the representation comprises expression levels of target sequences corresponding to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences depicted in Table 6, or of the subsets described herein, or of a combination thereof. In some instances, the target sequences correspond to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences selected from SEQ ID NOs.: 1-903. In other instances, the target sequences correspond to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences selected from SEQ ID NOs.: 1-352. Alternatively, the target sequences correspond to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences selected from SEQ ID NOs.: 353-441. In some embodiments, the representation of the measured expression level(s) may take the form of a linear or nonlinear combination of expression levels of the target sequences of interest. The patient report may be provided in a machine (e.g., a computer) readable format and/or in a hard (paper) copy. The report can also include standard measurements of expression levels of said plurality of target sequences from one or more sets of patients with known disease status and/or outcome. The report can be used to inform the patient and/or treating physician of the expression levels of the expressed target sequences, the likely medical diagnosis and/or implications, and optionally may recommend a treatment modality for the patient.

Also provided are representations of the gene expression profiles useful for treating, diagnosing, prognosticating, and otherwise assessing disease. In some embodiments, these profile representations are reduced to a medium that can be automatically read by a machine such as computer readable media (magnetic, optical, and the like). The articles can also include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a readable storage form having computer instructions for comparing gene expression profiles of the portfolios of genes described above. The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the profiles can be recorded in different representational format. A graphical recordation is one such format. Clustering algorithms can assist in the visualization of such data.

Exemplary Embodiments

Disclosed herein, in some embodiments, is a method for diagnosing, predicting, and/or monitoring a status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is a non-coding RNA transcript selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) for diagnosing, predicting, and/or monitoring a status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-352. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some instances, the plurality of targets comprises at least about 25% non-coding targets. In some instances, the plurality of targets comprises at least about 5 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 10 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 15 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 20 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 30 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 40 coding targets and/or non-coding targets. In some instances, the plurality of targets comprise at least about 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425 coding targets and/or non-coding targets. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein, is some embodiments, is a method for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets comprises a coding target and a non-coding target; and (ii) the non-coding target is not selected from the group consisting of a miRNA, an intronic sequence, and a UTR sequence; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. Alternatively, the plurality of targets comprises a coding and/or non-coding target selected from SEQ ID NOs.: 1-352. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the non-coding RNA is not a siRNA. In some embodiments, the non-coding RNA is not a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein, in some embodiments, is a method for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets consist essentially of a non-coding target or a non-exonic transcript; wherein the non-coding target is selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, and wherein the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding target is an intronic sequence or partially overlaps with an intronic sequence. In some embodiments, the non-coding target is a UTR sequence or partially overlaps with a UTR sequence. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target is a nucleic acid sequence. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a miRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment.

Further disclosed herein, in some embodiments, is a method for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed, in some embodiments, is a method for determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets comprises a coding target and a non-coding target; and (ii) the non-coding target is a non-coding RNA transcript selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) determining the treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein, in some embodiments, is a method of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets comprises a coding target and a non-coding target; (ii) the non-coding target is not selected from the group consisting of a miRNA, an intronic sequence, and a UTR sequence; and (b) determining the treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, the non-coding RNA is not a siRNA. In some embodiments, the non-coding RNA is not a snoRNA. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen

Further disclosed herein, in some embodiments, is a method of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets consist essentially of a non-coding target; wherein the non-coding target is selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, and wherein the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) determining the treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding target is an intronic sequence or partially overlaps with an intronic sequence. In some embodiments, the non-coding target is a UTR sequence or partially overlaps with a UTR sequence. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target is a nucleic acid sequence. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a miRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen

Further disclosed herein, in some embodiments, is a method of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated; and (b) determining a treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen

The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject having or suspected of having cancer. In some embodiments, such methods involve contacting a test sample with the probe sets, probes, ICE blocks, classifiers, PSRs, and primers (either in solution or immobilized) under conditions that permit hybridization of the probe(s) or primer(s) to any target nucleic acid(s) present in the test sample and then detecting any probe:target duplexes or primer:target duplexes formed as an indication of the presence of the target nucleic acid in the sample. Expression patterns thus determined can then be compared to one or more reference profiles or signatures. Optionally, the expression pattern can be normalized.

The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to determine the status or outcome of a cancer. The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to classify the cancer as recurrent or non-recurrent. The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to classify the cancer as metastatic or non-metastatic. In some embodiments, such methods involve the specific amplification of target sequences nucleic acid(s) present in the test sample using methods known in the art to generate an expression profile or signature which is then compared to a reference profile or signature.

In some embodiments, the invention further provides for prognosing patient outcome, predicting likelihood of recurrence after prostatectomy and/or for designating treatment modalities.

In one embodiment, the methods generate expression profiles or signatures detailing the expression of the target sequences having altered relative expression with different cancer outcomes. In some embodiments, the methods detect combinations of expression levels of sequences exhibiting positive and negative correlation with a disease status. In one embodiment, the methods detect a minimal expression signature.

The gene expression profiles of each of the target sequences comprising the portfolio can be fixed in a medium such as a computer readable medium. This can take a number of forms. For example, a table can be established into which the range of signals (e.g., intensity measurements) indicative of disease or outcome is input. Actual patient data can then be compared to the values in the table to determine the patient samples diagnosis or prognosis. In a more sophisticated embodiment, patterns of the expression signals (e.g., fluorescent intensity) are recorded digitally or graphically.

The expression profiles of the samples can be compared to a control portfolio. The expression profiles can be used to diagnose, predict, or monitor a status or outcome of a cancer. For example, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise diagnosing or detecting a cancer, cancer metastasis, or stage of a cancer. In other instances, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise predicting the risk of cancer recurrence. Alternatively, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise predicting mortality or morbidity.

Further disclosed herein are methods for characterizing a patient population. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying the expression level for a plurality of targets in the sample; and (c) characterizing the subject based on the expression level of the plurality of targets. In some instances, the plurality of targets comprises one or more coding targets and one or more non-coding targets. In some instances, the coding target comprises an exonic region or a fragment thereof. The non-coding targets can comprise a non-exonic region or a fragment thereof. Alternatively, the non-coding target may comprise the UTR of an exonic region or a fragment thereof. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some instances, the method may further comprise diagnosing a cancer in the subject. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some instances, characterizing the subject comprises determining whether the subject would respond to an anti-cancer therapy. Alternatively, characterizing the subject comprises identifying the subject as a non-responder to an anti-cancer therapy. Optionally, characterizing the subject comprises identifying the subject as a responder to an anti-cancer therapy.

Further disclosed herein are methods for selecting a subject suffering from a cancer for enrollment into a clinical trial. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying the expression level for a plurality of targets in the sample; and (c) characterizing the subject based on the expression level of the plurality of targets. In some instances, the plurality of targets comprises one or more coding targets and one or more non-coding targets. In some instances, the coding target comprises an exonic region or a fragment thereof. The non-coding targets can comprise a non-exonic region or a fragment thereof. Alternatively, the non-coding target may comprise the UTR of an exonic region or a fragment thereof. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some instances, the method may further comprise diagnosing a cancer in the subject. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some instances, characterizing the subject comprises determining whether the subject would respond to an anti-cancer therapy. Alternatively, characterizing the subject comprises identifying the subject as a non-responder to an anti-cancer therapy. Optionally, characterizing the subject comprises identifying the subject as a responder to an anti-cancer therapy.

Further disclosed herein are probe sets comprising one or more probes, wherein the one or more probes hybridize to one or more targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 2 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. Alternatively, or additionally, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 3 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 25 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, or 425 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In other instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, or 900 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof.

In some instances, the probe sets disclosed herein comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to one or more targets located on chr2q31.3. In some instances, the one or more targets located on chr2q31.3 selected from SEQ ID NOs.: 262-291.

In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903.

Further disclosed herein are classifiers comprising one or more targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the classifiers comprise at least about 2 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. Alternatively, or additionally, the classifiers comprise at least about 3 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 25 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the classifiers comprise at least about 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, or 425 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In other instances, the classifiers comprise at least about 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, or 900 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the classifiers comprise a classifier selected from Table 17. Alternatively, or additionally, the classifiers comprise a classifier selected from Table 19.

In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the classifiers comprise one or more targets located on chr2q31.3. In some instances, the one or more targets located on chr2q31.3 selected from SEQ ID NOs.: 262-291.

In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903.

In some instances, the classifiers disclosed herein have an AUC value of at least about 0.50. In other instances, the classifiers disclosed herein have an AUC value of at least about 0.55. The classifiers disclosed herein can have an AUC value of at least about 0.60. Alternatively, the classifiers disclosed herein have an AUC value of at least about 0.65. In some instances, the classifiers disclosed herein have an AUC value of at least about 0.70. In other instances, the classifiers disclosed herein have an AUC value of at least about 0.75. The classifiers disclosed herein can have an AUC value of at least about 0.80. Alternatively, the classifiers disclosed herein have an AUC value of at least about 0.85. The classifiers disclosed herein can have an AUC value of at least about 0.90. In some instances, the classifiers disclosed herein have an AUC value of at least about 0.95.

The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 50%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 55%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 60%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 65%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 68%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 69%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 70%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 71%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 72%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 73%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 74%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 75%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 76%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 77%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 78%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 79%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 80%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 81%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 82%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 83%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 84%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 85%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 86%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 87%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 88%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 90%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 93%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 95%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 97%.

Disclosed herein, in some embodiments, are methods for diagnosing, predicting, and/or monitoring a status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for one or more targets, wherein the one or more targets are based on a genomic classifier; and (b) for diagnosing, predicting, and/or monitoring a status or outcome of a cancer based on the expression levels of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Further disclosed herein, in some embodiments, are methods of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a one or more targets, wherein the one or more targets are based on a genomic classifier; and (b) determining the treatment for a cancer based on the expression levels of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Further disclosed herein are methods for characterizing a patient population. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying an expression level in a sample from the subject for a one or more targets, wherein the one or more targets are based on a genomic classifier; and (c) characterizing the subject based on the expression level of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Further disclosed herein are methods for selecting a subject suffering from a cancer for enrollment into a clinical trial. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying an expression level in a sample from the subject for a one or more targets, wherein the one or more targets are based on a genomic classifier; and (c) characterizing the subject based on the expression level of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Disclosed herein, in some embodiments, is a system for analyzing a cancer, comprising (a) a probe set comprising a plurality of probes, wherein the plurality of probes comprises (i) a sequence that hybridizes to at least a portion of a non-coding target; or (ii) a sequence that is identical to at least a portion of a non-coding target; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target hybridized to the probe in a sample from a subject suffering from a cancer. In some instances, the plurality of probes further comprises a sequence that hybridizes to at least a portion of a coding target. In some instances, the plurality of probes further comprises a sequence that is identical to at least a portion of a coding target. The coding target and/or non-coding target can be selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. The coding target and/or non-coding target can comprise a sequence selected from SEQ ID NOs.: 1-903. The coding target and/or non-coding target can comprise any of the coding targets and/or non-coding targets disclosed herein.

In some instances, the system further comprises an electronic memory for capturing and storing an expression profile. The system can further comprise a computer-processing device, optionally connected to a computer network. The system can further comprise a software module executed by the computer-processing device to analyze an expression profile. The system can further comprise a software module executed by the computer-processing device to compare the expression profile to a standard or control. The system can further comprise a software module executed by the computer-processing device to determine the expression level of the target. In some instances, the system further comprises a machine to isolate the target or the probe from the sample. The system can further comprise a machine to sequence the target or the probe. The system can further comprise a machine to amplify the target or the probe. Alternatively, or additionally, the system comprises a label that specifically binds to the target, the probe, or a combination thereof. The system can further comprise a software module executed by the computer-processing device to transmit an analysis of the expression profile to the individual or a medical professional treating the individual. In some instances, the system further comprises a software module executed by the computer-processing device to transmit a diagnosis or prognosis to the individual or a medical professional treating the individual.

The plurality of probes can hybridize to at least a portion of a plurality or targets. Alternatively, or additionally, the plurality of probes can comprise a sequence that is identical to at least a portion of a sequence of a plurality of targets. The plurality of targets can be selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In some instances, the plurality of targets comprise at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In other instances, the plurality of targets comprise at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. The plurality of targets can comprise at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. Alternatively, the plurality of targets comprise at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. The sequences of the plurality of targets can comprise at least about 5 sequences selected from SEQ ID NOs: 1-903. The sequences of the plurality of targets can comprise at least about 10 sequences selected from SEQ ID NOs: 1-903. The sequences of the plurality of targets can comprise at least about 15 sequences selected from SEQ ID NOs: 1-903. The sequences of the plurality of targets can comprise at least about 20 sequences selected from SEQ ID NOs: 1-903.

The cancer can be selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some instances, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some instances, the cancer is a prostate cancer. In other instances, the cancer is a bladder cancer. Alternatively, the cancer is a thyroid cancer. The cancer can be a colorectal cancer. In some instances, the cancer is a lung cancer.

In some instances, disclosed herein, is a probe set for assessing a cancer status or outcome of a subject comprising a plurality of probes, wherein the probes in the set are capable of detecting an expression level of one or more targets. In some instances, the one or more targets are selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In some instances, the one or more targets comprise a non-coding target. The non-coding target can be an intronic sequence or partially overlaps with an intronic sequence. The non-coding target can comprise a UTR sequence or partially overlaps with a UTR sequence. The non-coding target can be a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated. Alternatively, or additionally, the one or more targets comprise a coding target. In some instances, the coding target is an exonic sequence. The non-coding target and/or coding target can be any of the non-coding targets and/or coding targets disclosed herein. The one or more targets can comprise a nucleic acid sequence. The nucleic acid sequence can be a DNA sequence. In other instances, the nucleic acid sequence is an RNA sequence.

Further disclosed herein is a kit for analyzing a cancer, comprising (a) a probe set comprising a plurality of plurality of probes, wherein the plurality of probes can detect one or more targets; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target sequences in a sample. In some instances, the kit further comprises a computer model or algorithm for correlating the expression level or expression profile with disease state or outcome. The kit can further comprise a computer model or algorithm for designating a treatment modality for the individual. Alternatively, the kit further comprises a computer model or algorithm for normalizing expression level or expression profile of the target sequences. The kit can further comprise a computer model or algorithm comprising a robust multichip average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based, nonlinear normalization, or a combination thereof.

Assessing the cancer status can comprise assessing cancer recurrence risk. Alternatively, or additionally, assessing the cancer status comprises determining a treatment modality. In some instances, assessing the cancer status comprises determining the efficacy of treatment.

The probes can be between about 15 nucleotides and about 500 nucleotides in length. Alternatively, the probes are between about 15 nucleotides and about 450 nucleotides in length. In some instances, the probes are between about 15 nucleotides and about 400 nucleotides in length. In other instances, the probes are between about 15 nucleotides and about 350 nucleotides in length. The probes can be between about 15 nucleotides and about 300 nucleotides in length. Alternatively, the probes are between about 15 nucleotides and about 250 nucleotides in length. In some instances, the probes are between about 15 nucleotides and about 200 nucleotides in length. In other instances, the probes are at least 15 nucleotides in length. Alternatively, the probes are at least 25 nucleotides in length.

In some instances, the expression level determines the cancer status or outcome of the subject with at least 40% accuracy. The expression level can determine the cancer status or outcome of the subject with at least 50% accuracy. The expression level can determine the cancer status or outcome of the subject with at least 60% accuracy. In some instances, the expression level determines the cancer status or outcome of the subject with at least 65% accuracy. In other instances, the expression level determines the cancer status or outcome of the subject with at least 70% accuracy. Alternatively, the expression level determines the cancer status or outcome of the subject with at least 75% accuracy. The expression level can determine the cancer status or outcome of the subject with at least 80% accuracy. In some instances, the expression level determines the cancer status or outcome of the subject with at least 64% accuracy.

Further disclosed herein is a method of analyzing a cancer in an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; and (b) comparing the expression profile from the sample to an expression profile of a control or standard.

Disclosed herein, in some embodiments, is a method of diagnosing cancer in an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) diagnosing a cancer in the individual if the expression profile of the sample (i) deviates from the control or standard from a healthy individual or population of healthy individuals, or (ii) matches the control or standard from an individual or population of individuals who have or have had the cancer.

Further disclosed herein is a method of predicting whether an individual is susceptible to developing a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the susceptibility of the individual for developing a cancer based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Also disclosed herein is a method of predicting an individual's response to a treatment regimen for a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the individual's response to a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Disclosed herein is a method of prescribing a treatment regimen for a cancer to an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) prescribing a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some instances, the one or more targets are selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In some instances, the one or more targets comprise a non-coding target. The non-coding target can be an intronic sequence or partially overlaps with an intronic sequence. The non-coding target can comprise a UTR sequence or partially overlaps with a UTR sequence. The non-coding target can be a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated. Alternatively, or additionally, the one or more targets comprise a coding target. In some instances, the coding target is an exonic sequence. The non-coding target and/or coding target can be any of the non-coding targets and/or coding targets disclosed herein. The one or more targets can comprise a nucleic acid sequence. The nucleic acid sequence can be a DNA sequence. In other instances, the nucleic acid sequence is an RNA sequence. The targets can be differentially expressed in the cancer.

The methods disclosed herein can further comprise a software module executed by a computer-processing device to compare the expression profiles. In some instances, the methods further comprise providing diagnostic or prognostic information to the individual about the cardiovascular disorder based on the comparison. In other instances, the method further comprises diagnosing the individual with a cancer if the expression profile of the sample (i) deviates from the control or standard from a healthy individual or population of healthy individuals, or (ii) matches the control or standard from an individual or population of individuals who have or have had the cancer. Alternatively, or additionally, the methods further comprise predicting the susceptibility of the individual for developing a cancer based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. The methods disclosed herein can further comprise prescribing a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some instances, the methods disclosed herein further comprise altering a treatment regimen prescribed or administered to the individual based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. In other instances, the methods disclosed herein further comprise predicting the individual's response to a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. The deviation can be the expression level of one or more targets from the sample is greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. Alternatively, the deviation is the expression level of one or more targets from the sample is at least about 30% greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. In other instances, the deviation is the expression level of one or more targets from the sample is less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. The deviation can be the expression level of one or more targets from the sample is at least about 30% less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals.

The methods disclosed herein can further comprise using a machine to isolate the target or the probe from the sample. In some instances, the method further comprises contacting the sample with a label that specifically binds to the target, the probe, or a combination thereof. The method can further comprise contacting the sample with a label that specifically binds to a target selected from Table 6.

In some instances, the method further comprises amplifying the target, the probe, or any combination thereof. Alternatively, or additionally, the method further comprises sequencing the target, the probe, or any combination thereof. Sequencing can comprise any of the sequencing techniques disclosed herein. In some instances, sequencing comprises RNA-Seq.

The methods disclosed herein can further comprise converting the expression levels of the target sequences into a likelihood score that indicates the probability that a biological sample is from a patient who will exhibit no evidence of disease, who will exhibit systemic cancer, or who will exhibit biochemical recurrence.

EXAMPLES Example 1: Non-Coding RNAs Discriminate Clinical Outcomes in Prostate Cancer

In this study, we performed whole-transcriptome analysis of a publicly available dataset from different types of normal and cancerous prostate tissue and found numerous previously unreported ncRNAs that can discriminate between clinical disease states. We found, by analysis of the entire transcriptome, differentially expressed ncRNAs that accurately discriminated clinical outcomes such as BCR and metastatic disease.

Materials and Methods

Microarray and Clinical Data

The publically available genomic and clinical data was generated by the Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate Oncogenome Project, previously reported by (Taylor et al., 2010). The Human Exon arrays for 131 primary prostate cancer, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at at the world wide web at ncbi.nlm.nih.gov/geo/series GSE21034. The patient and specimen details for the primary and metastases tissues used in this study were summarized in Table 2. For the analysis of the clinical data, the following ECE statuses were summarized to be concordant with the pathological stage: inv-capsule: ECE, focal: ECE+, established: ECE+.

Microarray Pre-Processing

Normalization and Summarization

After removal of the cell line samples, the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors (McCall M N, et al., 2010, Biostatistics, 11:254-53) was used to normalize and summarize the 179 microarray samples. These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369).

Sample Subsets

The normalized and summarized data were partitioned into three groups. The first group contained the matched samples from primary localized prostate cancer tumor and normal adjacent samples (n=58) (used for the normal versus primary comparison). The second group contained all of the samples from metastatic tumors (n=19) and all of the localized prostate cancer specimens which were not matched with normal adjacent samples (n=102) (used for the primary versus metastasis comparison). The third group contained all of the samples from metastatic tumors (n=19) and all of the normal adjacent samples (n=29) (used for the normal versus metastasis comparison).

Feature Selection

Probe sets comprising one or more probes that did not align uniquely to the genome were annotated as ‘unreliable’ and were excluded from further analysis. After cross hybridization, the PSRs corresponding to the remaining probe sets were subjected to univariate analysis and used in the discovery of differentially expressed PSRs between the labeled groups (primary vs. metastatic, normal adjacent vs. primary and normal versus metastatic). For this analysis, the PSRs were selected as differentially expressed if their Holm adjusted t-test P-value was significant (<0.05).

Feature Evaluation and Model Building

Multidimensional-scaling (Pearson's distance) was used to evaluate the ability of the selected features to segregate samples into clinically relevant clusters based on metastatic events and Gleason scores on the primary samples.

A k-nearest-neighbour (KNN) model (k=1, Pearson's correlation distance metric) was trained on the normal and metastatic samples (n=48) using only the features which were found to be differentially expressed between these two groups.

Re-Annotation of the Human Exon Microarray Probe Sets

In order to properly assess the nature of the PSRs found to be differentially expressed in this study, we re-annotated the PSRs using the xmapcore R package (Yates, 2010) as follows: (i) a PSR was re-annotated as coding, if the PSR overlaps with the coding portion of a protein-coding exon, (ii) a PSR was re-annotated as non-coding, if the PSR overlaps with an untranslated region (UTR), an intron, an intergenic region or a non protein-coding transcript, and (iii) a PSR was re-annotated as non-exonic, if the PSR overlaps with an intron, an intergenic region or a non protein-coding transcript. Further annotation of non-coding transcripts was pursued using Ensembl Biomart.

Statistical Analysis

Survival analysis for biochemical recurrence (BCR) and logistic regression for clinical recurrence were performed using the ‘survival’ and ‘lrm’ packages in with default values.

Results

Re-Annotation and Categorization of Coding and Non-Coding Differentially Expressed Features

Previous transcriptome-wide assessments of differential expression on prostate tissues in the post-prostatectomy setting have been focused on protein-coding features (see Nakagawa et al., 2008 for a comparison of protein-coding gene-based panels). Human Exon Arrays provided a unique opportunity to explore the differential expression of non-coding parts of the genome, with 75% of their probe sets falling in regions other than protein coding sequences. In this study, we used the publicly available Human Exon Array data set from normal, localized primary and metastatic tissues generated by the MSKCC Prostate Oncogenome Project to explore the potential of non-coding regions in prostate cancer prognosis. Previous attempts on this dataset focused only on mRNA and gene-level analysis and concluded that expression analysis was inadequate for discrimination of outcome groups in primary tumors (Taylor et al., 2010). In order to assess the contribution of ncRNA probe sets in differential expression analysis between sample types, we re-assessed the annotation of all PSRs found to be differentially expressed according to their genomic location and categorized them into coding, non-coding and non-exonic. Briefly, a PSR was classified as coding if it fell in a region that encoded for a protein-coding transcript. Otherwise, the PSR was annotated as non-coding. The ‘non-exonic’ group referred to a subset of the non-coding that excluded all PSRs that fell in UTRs.

Based on the above categorization, we assessed each set for the presence of differentially expressed features for each possible pairwise comparison (e.g. primary versus normal, normal versus metastatic and primary versus metastatic). The majority of the differentially expressed PSRs were labeled as ‘coding’ for a given pairwise comparison (60%, 59% and 53% for normal-primary, primary-metastatic and normal-metastatic comparisons, respectively). For each category, the number of differentially expressed features was highest in normal versus metastatic tissues, which was expected since the metastatic samples have likely undergone major genomic alterations through disease progression as well as possible different expression patterns from interactions with tissues they have metastasized to (FIG. 1). Additionally, for each category there were a significant number of features that were specific to each pairwise comparison. For example, 22% of the coding features were specific to the differentiation between normal and primary and 9% were specific to the primary versus metastatic comparison. The same proportions were observed for the non-coding and non-exonic categories, suggesting that different genomic regions may play a role in the progression from normal to primary and from primary to metastatic.

Within the non-coding and non-exonic categories, the majority of the PSRs were ‘intronic’ for all pairwise comparisons (see FIGS. 2a, 2b and 2c for non-exonic). Also, a large proportion of the PSRs fell in intergenic regions. Still, hundreds of PSRs were found to lie within non-coding transcripts, as reflected by the ‘NC Transcript’ segment in FIG. 2. The non-coding transcripts found to be differentially expressed in each pairwise comparison were categorized using the ‘Transcript Biotype’ annotation of Ensembl. For all pairwise comparisons the ‘processed transcript’, ‘lincRNA’, ‘retained intron’, and ‘antisense’ were the most prevalent (FIG. 2d , FIG. 2e and FIG. 2f ; see Table 3 for a definition of each transcript type). Even though ‘processed transcript’ and ‘retained intron’ categories were among the most frequent ones, they have a very broad definition.

Previous studies have reported several long non-coding RNAs to be differentially expressed in prostate cancer (Srikantan et al., 2000; Berteaux et al., 2004; Petrovics et al., 2004; Lin et al., 2007; Poliseno et al., 2010; Yap et al., 2010; Chung et al., 2011; Day et al., 2011). Close inspection of our data reveals that four of them (PCGEM1, PCA3, MALAT1 and H19) were differentially expressed (1.5 Median Fold Difference (MFD) threshold) in at least one pairwise comparison (Table 4). After adjusting the P-value for multiple testing however, only seven PSRs from these ncRNA transcripts remain significant (Table 4). In addition, we found two microRNA-encoding transcripts to be differentially expressed in primary tumour versus metastatic (MIR143, MIR145 and MIR221), two in normal versus primary tumour comparison (MIR205 and MIR7) and three in normal versus metastatic (MIR145, MIR205 and MIR221). All these miRNA have been previously reported as differentially expressed in prostate cancer (Clape et al., 2009; Barker et al., 2010; Qin et al., 2010; Szczyrba et al., 2010; Zaman et al., 2010).

Therefore, in addition to the handful of known ncRNAs, our analysis detected many other ncRNAs in regions (e.g., non-coding, non-exonic) that have yet to be explored in prostate cancer and may play a role in the progression of the disease from normal glandular epithelium through distant metastases of prostate cancer.

Assessment of Clinically Significant Prostate Cancer Risk Groups

Using multidimensional scaling (MDS) we observed that the non-exonic and non-coding subsets of features better segregated primary tumors from patients that progressed to metastatic disease than the coding subset (FIG. 3). Similarly, we found the non-exonic and non-coding subset better discriminated high and low Gleason score samples than the coding subset (FIG. 5). In order to assess the prognostic significance of differentially expressed coding, non-coding and non-exonic features, we developed a k-nearest neighbour (KNN) classifier for each group, trained using features from the comparison of normal and metastatic tissue types (see methods). Next, we used unmatched primary tumors (e.g. removing those tumors that had a matched normal in the training subset) as an independent validation set for the KNN classifier. The higher the KNN score (ranging from 0 to 1), the more likely the patient will be associated to worse outcome. Each primary tumor in the validation set was classified by KNN as either more similar to normal or metastatic tissue. Kaplan-Meier analysis of the two groups of primary tumor samples classified by KNN using the biochemical recurrence (BCR) end point (FIG. 4, ‘normal-like’=dark grey line, ‘metastatic-like’=light gray line) was done for KNN classifiers derived for each subset of features (e.g., coding, non-coding and non-exonic). As expected, primary tumors classified by KNN as belonging to the metastasis group had a higher rate of BCR. However, we found that for the KNN classifier derived using only the coding subset of features, no statistically significant differences in BCR-free survival were found using log-rank tests for significance (p<0.08) whereas they were highly significant for the non-coding (p<0.00005) and non-exonic (p<0.00003) KNN classifiers. Furthermore, multivariable logistic regression analysis to predict for patients that experienced metastatic disease (e.g., castrate or non-castrate resistant clinical metastatic patients) for each of the three KNN classifiers (e.g., coding, non-coding and non-exonic) was evaluated (Table 5). Adjusting the KNN classifiers for known prognostic clinical variables (e.g. SVI, SMS, Lymph Node Involvement (LNI), pre-treatment PSA values, ECE and Gleason score) revealed that the KNN based on coding feature set had an odds ratio of 2.5 for predicting metastatic disease, but this was not significant (χ², p<0.6). The KNN obtained based on the non-coding feature set had a much higher odds ratio of 16 though again being not statistically significant (χ², p<0.14). In multivariable analysis, only the KNN based solely on the non-exonic feature set had a statistically significant odds ratio of 30 (χ², p<0.05). These results suggest that significantly more predictive information can be obtained from analysis of non-exonic RNAs and that these may have the potential to be used as biomarkers for the prediction of a clinically relevant outcome in primary tumours after prostatectomy.

Discussion

One of the key challenges in prostate cancer was clinical and molecular heterogeneity (Rubin et al., 2011); therefore this common disease provides an appealing opportunity for genomic-based personalized medicine to identify diagnostic, prognostic or predictive biomarkers to assist in clinical decision making. There have been extensive efforts to identify biomarkers based on high-throughput molecular profiling such as protein-coding mRNA expression microarrays (reviewed in Sorenson and Orntoft, 2012), but while many different biomarkers signatures have been identified, none of them were actively being used in clinical practice. The major reason that no new biomarker signatures have widespread use in the clinic was because they fail to show meaningful improvement for prognostication over PSA testing or established pathological variables (e.g., Gleason).

In this study, we assessed the utility of ncRNAs, and particularly non-exonic ncRNAs as potential biomarkers to be used for patients who have undergone prostatectomy but were at risk for recurrent disease and hence further treatment would be considered. We identified many thousands of coding, non-coding and non-exonic RNAs differentially expressed between the different tissue specimens in the MSKCC Oncogenome Project. In a more focused analysis of these feature subset groups (derived from comparison of normal adjacent to primary tumor and metastatic prostate cancer), we found that the coding feature subsets contained substantially less prognostic information than their non-coding counterparts as measured by their ability to discriminate two clinically relevant end-points. First, we observed clustering of those primary tumors from patients that progressed to metastatic disease with true metastatic disease tissue when using the non-exonic features; this was not observed with the coding features. Next, Kaplan-Meier analysis between KNN classifier groups (e.g., more ‘normal-like’ vs. more ‘metastatic-like’) among primary tumors showed that only the non-coding and non-exonic feature sets had statistically significant BCR-free survival. Finally, multivariable analysis showed only the non-exonic feature subset KNN classifier was significant after adjusting for established prognostic factors including pre-operative PSA and Gleason scores with an odds ratio of 30 for predicting metastatic disease.

Based on these three main results, we concluded that non-exonic RNAs contain previously unrecognized prognostic information that may be relevant in the clinic for the prediction of cancer progression post-prostatectomy. Perhaps, the reason that previous efforts to develop new biomarker based predictors of outcome in prostate cancer have not translated into the clinic have been because the focus was on mRNA and proteins, largely ignoring the non-coding transcriptome.

These results add to the growing body of literature showing that the ‘dark matter’ of the genome has potential to shed light on tumor biology, characterize aggressive cancer and improve in the prognosis and prediction of disease progression.

Example 2: Method of Diagnosing a Leukemia in a Subject

A subject arrives at a doctor's office and complains of symptoms including bone and joint pain, easy bruising, and fatigue. The doctor examines the subject and also notices that the subject's lymph nodes were also swollen. Bone marrow and blood samples were obtained from the subject. Microarray analysis of the samples obtained from the subject reveal aberrant expression of a classifier disclosed herein comprising non-coding targets and coding targets and the subject was diagnosed with acute lymphoblastic leukemia.

Example 3: Method of Determining a Treatment for Breast Cancer in a Subject

A subject was diagnosed with breast cancer. A tissue sample was obtained from the subject. Nucleic acids were isolated from the tissue sample and the nucleic acids were applied to a probe set comprising at least ten probes capable of detecting the expression of at least one non-coding target and at least one coding target. Analysis of the expression level of the non-coding targets and coding targets reveals the subject has a tamoxifen-resistant breast cancer and gefitinib was recommended as an alternative therapy.

Example 4: Method of Determining the Prognosis for Pancreatic Cancer in a Subject

A subject was diagnosed with pancreatic cancer. A tissue sample was obtained from the subject. The tissue sample was assayed for the expression level of biomarkers comprising at least one non-coding target and at least one coding target. Based on the expression level of the non-coding target, it was determined that the pancreatic cancer has a high risk of recurrence.

Example 5: Method of Diagnosing a Prostate Cancer in a Subject

A subject arrives at a doctor's office and complains of symptoms including inability to urinate standing up, blood in urine, and dull, incessant pain in the pelvis and lower back. The doctor conducts a digital prostate exam and recommends that blood samples were obtained from the subject. The PSA was abnormal, a biopsy was ordered and microarray analysis of the blood and tissue samples obtained from the subject reveal aberrant expression of non-coding targets and the subject was diagnosed with prostate cancer.

Example 6: Method of Determining a Treatment for Lung Cancer in a Subject

A subject was diagnosed with non-small cell lung cancer (NSCLC). A tissue sample was obtained from the subject. Nucleic acids were isolated from the tissue sample and the nucleic acids were applied to a probe set comprising at least five probes capable of detecting the expression of at least one non-coding target. Analysis of the expression level of the non-coding targets reveals the subject has a cisplatin-resistant NSCLC and gemcitabine was recommended as an alternative therapy.

Example 7: Genome-Wide Detection of Differentially Expressed Coding and Non-Coding Transcripts and Clinical Significance in Prostate Cancer Using Transcript-Specific Probe Selection Regions

In this study, we performed whole-transcriptome analysis of a publicly available dataset from different types of normal and cancerous prostate tissue and found numerous differentially expressed coding and non-coding transcripts that discriminate between clinical disease states.

Materials and Methods

Microarray and Clinical Data

The publically available genomic and clinical data was generated by the Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate Oncogenome Project, previously reported by Taylor et al., 2010. The Human Exon arrays for 131 primary prostate cancers, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at the world wide web at ncbi.nlm.nih.gov/geo/series GSE21034. The patient and specimen details for the primary and metastases tissues used in this study were reported in Vergara I A, et al., 2012, Frontiers in Genetics, 3:23. For the analysis of the clinical data, the following ECE statuses were summarized to be concordant with the pathological stage: inv-capsule: ECE−, focal: ECE+, established: ECE+.

Microarray Pre-Processing

Normalization and Summarization

The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369) including all MSKCC samples. Normalization was done by the quantile normalization method and summarization by the robust weighted average method, as implemented in fRMA. Gene-level expression values were obtained by summarizing the probe selection regions (or PSRs) using fRMA and the corresponding Affymetrix Cluster Annotation (www.affymetrix.com/).

Sample Subsets

The normalized and summarized data was partitioned into three groups. The first group contains the samples from primary localized prostate cancer tumor and normal adjacent samples (used for the normal versus primary comparison). The second group contained all of the samples from metastatic tumors and all of the localized prostate cancer specimens (used for the primary versus metastasis comparison). The third group contained all of the samples from metastatic tumors and all of the normal adjacent samples (used for the normal versus metastasis comparison).

Detection of Transcript-Specific PSRs in Human Exon Microarray Probe Sets

Using the xmapcore R package (Yates, 2010), all exonic PSRs that were specific to only one transcript were retrieved, generating a total of 123,521 PSRs. This set of PSRs was further filtered in order to remove all those that correspond to a gene but such that (i) the gene has only one transcript, or (ii) the gene has multiple transcripts, but only one can be tested in a transcript-specific manner. Applying these filters reduced the total number of transcript-specific PSRs to 39,003 which were the main focus of our analysis.

Feature Selection

Based on the set of transcript specific PSRs, those annotated as ‘unreliable’ by the xmapcore package (Yates, 2010) (one or more probes do not align uniquely to the genome) as well as those not defined as class 1 cross-hybridizing by Affymetrix were excluded from further analysis (at the world wide web at affymetrix.com/analysis/index.affx). Additionally, those PSRs that present median expression values below background level for all of the three tissue types (normal adjacent, primary tumor and metastasis) were excluded from the analysis. The remaining PSRs were subjected to univariate analysis to discover those differentially expressed between the labeled groups (primary vs. metastatic, normal adjacent vs. primary and normal vs. metastatic). For this analysis, PSRs were selected as differentially expressed if their FDR adjusted t-test P-value was significant (<0.05) and the Median Fold Difference (MFD) was greater or equal than 1.2. The t-test was applied as implemented in the row t-tests function of the genefilter package (at the world wide web at bioconductor.org/packages/2.3/bioc/html/genefilter.html). The multiple testing corrections were applied using the p-adjust function of the stats package in R.

For a given transcript with two or more transcript-specific PSRs significantly differentially expressed, the one with the best P-value was chosen as representative of the differential expression of the transcript. In order to avoid complex regions, cases for which a transcript specific PSR would overlap with more than one gene (for example within the intron of another gene) were filtered out from the analysis.

Feature Evaluation and Model Building

A k-nearest-neighbour (KNN) model (k=1, Euclidean distance) was trained on the normal and metastatic samples (n=48) using only the top 100 features found to be differentially expressed between these two groups.

Statistical Analysis

Biochemical recurrence and metastatic disease progression end points were used as defined by the “BCR Event” and “Mets Event” columns of the supplementary material provided by (Taylor et al., 2010), respectively. Survival analysis for BCR was performed using the survfit function of the survival package.

Results

Detection of Transcript-Specific PSRs in Human Exon Arrays

Detection of transcript-specific differential expression was of high interest as different spliced forms of the same gene might play distinct roles during progression of a given disease. For example, in the case of prostate cancer, it has been recently reported that not only does the main transcript associated with the Androgen Receptor (AR) gene play a role in prostate cancer, but other variants, such as v567, function in a distinct manner to that of the main spliced form (Chan et al, J. Biol. Chem, 2012; Li et al, Oncogene, 2012; Hu et al, Prostate, 2011). Affymetrix HuEx arrays provided a unique platform to test the differential expression of the vast majority of exonic regions in the genome. Based on Ensembl v62 and xmapcore (Yates et al 2010), there were 411,681 PSRs that fell within exons of protein-coding and non-coding transcripts. Within this set, a subset of 123,521 PSRs (˜10% of the PSRs in the array) allowed for the unequivocal testing of the differential expression of transcripts, as they overlap with the exon of only one transcript. These PSRs, which we called transcript-specific PSRs (TS-PSRs), cover 49,302 transcripts corresponding to 34,599 genes. In this study, we used the publicly available Human Exon Array data set generated by the MSKCC Prostate Oncogenome Project to explore the transcript-specific differential expression through progression of prostate cancer from normal, primary tumor and metastatic tissues. In particular, we focus on the assessment of two or more different transcripts within a gene in a comparative manner. Hence, the set of 123,521 TS-PSRs was further filtered in order to remove all those that correspond to a gene, such that (i) the gene has only one transcript (69,591 TS-PSRs; FIG. 15A), or (ii) the gene has multiple transcripts, but only one can be tested in a transcript-specific manner (14,927 TS-PSRs; FIG. 15B). This generated a final set of 39,003 TS-PSRs corresponding to 22,517 transcripts and 7,867 genes that were used as the basis of this analysis (FIG. 15C).

Differential Expression of Coding and Non-Coding Transcripts Through Prostate Cancer Progression

Assessment of the defined set of TS-PSRs yielded 881 transcripts that were differentially expressed between any pairwise comparison on the normal adjacent, primary tumor and metastatic samples (see methods; FIG. 11). These 881 transcripts corresponded to 680 genes, due to genes with two or more transcripts differentially expressed at the same or different stages of cancer progression. Interestingly, 371 (42%) of the differentially expressed transcripts were non-coding. Inspection of their annotation reveals that they fell into several non-coding categories, the most frequent being “retained intron” (n=151) and “processed transcript” (n=186). Additionally, most of the genes associated with these non-coding transcripts were coding, (i.e. they encode at least one functional protein). Examples of non-coding genes with differentially expressed transcripts found in this dataset include the lincRNAs PART1 (Prostate Androgen-Regulated Transcript 1, Lin et al 2000, Cancer Res), MEG3 (Ribarska et al 2012), the PVT1 oncogene, located in the 8q24 susceptibility region (Meyer et al 2011, PLoS Genetics), and the testis-specific lincRNA TTTY10. Other ncRNAs include the small nucleolar RNA host gene 1 (SNHG1) which has been suggested as a useful biomarker for disease progression (Berretta and Moscato, 2011, PLoS ONE), as well as GAS5, located in the 1q25 risk loci (Nam et al 2008; Prstate Cancer Prostatic Dis). Additionally, three pseudogenes were found differentially expressed in this dataset: EEF1DP3, located in a region previously found to be a focal deletion in metastatic tumors (Robbins et al 2011, Genome Research), the Y-linked pseudogene PRKY, which has been found expressed in prostate cancer cell lines (Dasari et al, 2000, Journal of Urology) and PABPC4L.

In addition to the non-coding genes, many coding genes presented one or more non-coding transcripts that were differentially expressed. Table 7 provides a list of genes that have been shown to participate in prostate cancer and that contain one or more non-coding transcripts differentially expressed according to our analysis, including the Androgen Receptor (Chan et al, J. Biol. Chem, 2012; Li et al, Oncogene, 2012; Hu et al, Prostate, 2011), ETV6 (Kibel et al, 2000, The Journal of Urology) and the fibroblast growth receptors FGFR1 and FGFR2 (Naimi et al 2002, The Prostate). Focusing on the individual transcripts of genes known to play a role in prostate cancer progression and their coding ability might shed light on the mechanisms in which each transcript was involved. Overall, the set of non-coding transcripts in both coding and non-coding genes reported here add to the current stream of evidence showing that non-coding RNA molecules may play a significant role in cancer progression (Vergara et al 2012, Kapranov et al 2010).

Genes with Multiple Transcripts Differentially Expressed Through Prostate Cancer Progression

The majority of the 881 differentially expressed transcripts came from the comparison between normal adjacent and metastatic samples, in agreement with previous analyses of differential expression of tissue on the MSKCC dataset (Vergara et al., 2012). As shown in FIG. 11, 28 of the differentially expressed transcripts were found throughout the progression from normal adjacent through primary tumor to metastasis, with 22 of them across all three pairwise comparisons (Table 8, top). These 22 transcripts reflected instances of a significant increase or decrease of expression through all stages in the same direction (i.e. always upregulated or downregulated). The remaining 6 transcripts found to be differentially expressed in the normal adjacent vs primary tumor as well as in the primary tumor versus metastatic sample comparison (but not in the normal adjacent versus metastatic samples comparison) were a reflection of differential expression that occurs in different directions in the progression from normal to primary tumor compared to that from primary tumor to metastasis, suggesting that these transcripts play a major role during the primary tumor stage of the disease (Table 8, bottom). In particular, within this set of 28 transcripts there were two AR-sensitive genes, FGFR2 and NAMPT, that presented two transcripts that were differentially expressed throughout progression. In the case of the FGFR2 gene (a fibroblast growth receptor), our observation of significant decrease in expression from normal to metastasis was in agreement with a previous study that shows downregulation of isoforms ‘b’ and ‘c’ to be associated with malignant expression in prostate (Naimi et al, 2002, The Prostate). In the case of NAMPT (a nicotinamide phosphoribosyltransferase), the two transcripts showed a peak of expression in the primary tumor tissues compared to normal and metastasis; the rise in primary tumors compared to normal was in full agreement with previously reported elevation of expression during early prostate neoplasia for this gene (Wang et al, 2011, Oncogene). For both genes, the transcripts were differentially expressed in the same direction as the tumor progresses, suggesting that both transcripts were functioning in a cooperative manner. In order to determine if this was a general pattern of the transcripts analyzed here, all of the genes for which at least two transcripts presented differential expression were inspected (FIG. 12). Among the 140 genes for which we find such cases, there was a clear trend for groups of transcripts of the same gene to express in the same direction as the tumor progresses. Two exceptions that were found were genes CALD1 and AGR2. For both of them, the differential expression of one of their transcripts in the progression from primary tumor to metastasis went in the opposite direction compared to the other transcripts. In the case of AGR2, transcript AGR2-001 was downregulated in metastasis compared to primary tumor, whereas AGR2-007 was upregulated. This observation was in agreement with previous reports on a short and long isoform of the same gene (Bu et al, 2011, The Prostate). Even though the correspondence of the short and long isoforms to those annotated in Ensembl was not straightforward, alignment of the primers used in Bu et al. (2011) showed overlapping of the short isoform with AGR2-001, and of the long isoform with AGR2-007, which agreed with their divergent expression patterns. In the case of CALD1, while transcript CALD1-012 was upregulated, CALD1-005 and CALD1-008 were downregulated in the progression from primary tumor to metastasis. A previous study on 15 prostate cancer samples showed that CALD1-005 was downregulated in metastatic samples compared to primary tumor, in agreement with our results.

Transcripts Level Resolution of Differential Expression on Fully Tested Genes

Of the 7,867 genes for which one or more transcripts were assessed in this analysis, 1,041 genes were such that all of their transcripts have at least one TS-PSR. Of these, 92 genes were such that at least one of their transcripts was found to be differentially expressed in any pairwise comparison among normal adjacent, primary tumor and metastatic samples. As depicted in FIG. 13, the majority of the genes only have one differentially expressed transcript. This included cases like KCNMB1 and ASB2, two genes that have been previously reported to be differentially expressed in prostate cancer, but for which no observation at the transcript level has been made (Zhang et al 2005, Cancer Genomics and Proteomics; Yu et al 2004, JCO). In the case of KCNMB1, only transcript KCNMB1-001 of the two transcripts was found to be differentially expressed, whereas for ASB2, only transcript ASB2-202 was found to be differentially expressed of the three transcripts annotated for this gene. Also, other genes presented differential expression of their non-coding transcripts only. One example of this was PCP4 (also known as PEP-19), a gene known to be expressed in prostate tissue (Kanamori et al 2003, Mol. Hum. Reprod).

In addition to the expression profile of each transcript for these 92 genes, FIG. 13 shows the corresponding summarized gene-level expression profile for each gene. Of these, only 18 genes present differential expression at the gene level, clearly illustrating that summarization of expression can result in significant loss of information.

TS-PSRs Constitute a Clinically Significant Prostate Cancer Risk Group

In order to assess the prognostic significance of the differentially expressed transcripts, the corresponding TS-PSRs were used to train a KNN classifier on normal and metastatic samples and validated on the primary tumors, such that each primary tumor sample was classified as normal or metastatic based on its distance to the normal and metastatic groups. The higher the KNN score (ranging from 0 to 1), the more likely the patient will be associated to worse outcome. As shown in FIG. 14, the difference in the Kaplan-Meier (KM) curves for the two groups was statistically significant using biochemical recurrence as an endpoint and was comparable to that of the Kattan nomogram (Kattan et al 1999). Further assessment of coding and non-coding differentially expressed transcripts showed both sets to yield statistically significant differences in their KM curves. The corresponding set of differentially expressed genes still presented a statistically significant difference of the KM curves, despite the observed loss of information from the summarization when comparing different tissue types. A multivariable logistic regression analysis of the groups of transcripts and genes differentially expressed showed that the transcripts remain highly statistically significant after adjusting for the Kattan nomogram (p<0.005), whereas the genes resulted in borderline significance after adjustment (p=0.05) (Table 9). These results suggest that differential expression of specific transcripts have unique biomarker potential that adds value to that of classifiers based on clinicopathological variables such as nomograms.

Example 8: Differentially Expressed Non-Coding RNAs in Chr2q31.3 has Prognostic Potential and Clinical Significance Based on Fresh Frozen Samples

Methods

The publicly available expression profiles of normal and prostate tumor samples, Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor et al., 2010) were downloaded from at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034. The Human Exon arrays for 131 primary prostate cancer, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at the world wide web at ncbi.nlm.nih.gov/geo/series GSE21034. Information on Tissue samples, RNA extraction, RNA amplification and hybridization were disclosed in Taylor et al., 2010. The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Feature selection was conducted using a t-test for differential expression on the 857 Probe Selection Regions (or PSRs) within chr2q31.3 region. A PSR was regarded as significantly differentially expressed if the P-value of the t-test was lower than 0.05 in any of the following comparisons: BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM. Additionally, a PSR was found significant if the P-values of the differences between the KM curves for BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM was lower than 0.05. Table 6, SEQ ID NOs.: 262-291 provides the detail of which comparison(s) yielded the PSR as significant.

Non-Coding Analysis

Using annotation data from the human genome version hg19/GRCh37 (Ensembl annotation release 62) and xmapcore (Yates, 2007), we categorized the PSRs depending on the chromosomal location and orientation with respect to coding and non-coding gene annotation as Coding, Non-coding (UTR), Non-coding (ncTranscript), Non-coding (Intronic), Non-coding (CDS_Antisense), Non-coding (UTR_Antisense), Non-coding (ncTranscript_Antisense), Non-coding (Intronic_Antisense), Non-coding (Intergenic). We additionally used xmapcore to annotate the gene symbol, gene synonym, Ensembl gene ID and biological description for any PSRs that overlapped with a transcript; this excludes alignments to non-coding (non-unique) and non-coding (intergenic) sequences.

Ontology Enrichment Analysis

DAVID Bioinformatics tool was used to assess enrichment of ontology terms (Huang da W, et al., 2009, Nat Protoc, 4:44-57; Huang da W, et al., 2009, Nucleic Acids Res, 37:1-13).

Results

Based on the criteria defined above, 429 PSRs were found to be differentially expressed within chr2q31.3 (Table 6, SEQ ID NOs.: 262-291). Of these 429 PSRs, the vast majority were non-coding, with only 20% mapping to a protein-coding region of a gene (FIG. 16). The most represented groups in the non-coding category were Intronic PSRs (26%) and Intergenic PSRs (27%). The fact that one of the largest groups was the intergenic one demonstrates that chr2q31.3 had significant unexplored prognostic potential. In fact, DAVID assessment of the functional annotation of these PSRs yielded no significant Gene Ontology terms for Biological Processes, in agreement with the idea that DAVID was a tool built mostly upon protein-coding gene information.

Additionally, approximately 8% of the PSRs overlapped with transcripts that did not encode for a functional protein. The distribution of the non-coding transcripts according to Ensembl annotation (at the world wide web at ensembl.org) were as follows: 6 “processed transcript”, 3 “retained intron”, 7 “large intergenic non-coding RNA”, 4 “processed_pseudogene”, 1 “non-sense mediated decay” and 1 snoRNA.

In order to further assess the clinical significance of the selected PSRs, KM curves were built using Biochemical Recurrence (BCR), as endpoint. As depicted in FIG. 17, the PSR corresponding to the probe set ID 2518027 showed a statistically significant difference of the KM curves for BCR endpoint, further demonstrating the prognostic potential of this region.

Example 9: Digital Gleason Score Predictor Based on Differentially Expressed Coding and Non-Coding Features

In this study we evaluated the use of differentially expressed coding and non-coding features.

Methods

The publicly available expression profiles of normal and prostate tumor samples, Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor et al., 2010) were downloaded at at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034 and the German Cancer Research Center (DKFZ) (Brase et al., 2011) at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29079 were pooled and used to define a training set and a testing set. The training set consisted of all of the samples with a Gleason Score lower than 7 (hereafter called GS<7) and higher than 7 (hereafter called GS>7), whereas the testing set comprised all of the samples with a Gleason Score of 7 (hereafter called GS7). The group of GS7 patients was further split into 3+4 and 4+3 based on the Primary and Secondary Gleason Grades.

Information on tissue samples, RNA extraction, RNA amplification and hybridization can be found elsewhere (Taylor et al., 2010; Brase et al., 2011). The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Feature selection was done using a t-test for differential expression between those GS<7 and GS>7 samples. 102 Probe Selection Regions (PSRs) were kept after a Holm P-value adjustment threshold of 0.05. The top 12 PSRs were used to build a random forest classifier with the following parameters: mtry=1, nodesize=26, ntree=4000. The mtry and nodesize parameters were selected via the random forest tune function. The classifier generated with this methodology is hereafter called RF12.

Results

Of the 102 PSRs found differentially expressed, 43% of them were in coding regions (FIG. 18). The rest of the PSRs were distributed within introns, untranslated regions (or UTRs), non-coding transcripts or were non-unique. Non-unique PSRs composed 13% of the differentially expressed PSRs. Some of these PSRs required thorough manual assessment in order to understand their nature; while some of them could be annotated as non-unique due to the presence of allelic variants in the genome assembly, others likely provided differential expression information through the existence of copy-number variations. A partial list of the 102 PSRs identified can be found in Table 6, SEQ ID NOs.: 292-321.

Using the trained RF12 classifier on the GS<7 and GS>7 samples, each GS7 (3+4 and 4+3) sample was assigned a probability of risk. The RF12 score, which ranges from 0 to 1, is the percentage of decision trees in the random forest which label a given patient as having the Gleason grade of the profiled tissue as greater than 3. A higher RF12 score means a worse prognosis for a patient as correlated with Gleason score. The higher the probability, the higher the risk associated to the sample. As shown in FIG. 19A, the probability distributions of the 3+4 samples versus 4+3 samples were significantly different. Those samples with a primary Gleason grade of 3 tended to have a lower probability than those with a primary Gleason grade of 4, which was in agreement with a higher Gleason grade corresponding to a higher risk of prostate cancer progression. Assessment of RF12 performance yielded an accuracy of 74%, which was significantly different to the 61% accuracy that was achieved with a null model. The high performance of the RF12 classifier was confirmed with the AUC metric, yielding an AUC of 77%.

In order to further illustrate the prognostic potential and to assess the clinical significance of this classifier, KM curves on the groups predicted by RF12 were generated using the probability of BCR-free survival as endpoint. As shown in FIG. 19B, the difference between the low and high risk groups was statistically significant (p<0.01), demonstrating the ability of RF12 to discriminate between those samples from patients that were at high risk of progressing to biochemical recurrence versus those that were at low risk.

Example 10: KNN Models Based on PSR Genomic Subsets

In this study, Probe Selection Regions (PSRs) were annotated using xmapcore into the following categories: Intronic, Intergenic, Antisense, ncTranscript and Promoter Region. Antisense refers to a PSR being located in the opposite strand of a gene. Promoter Region was defined as the 2 kbp upstream region of a transcript, excluding the 5′UTR. Following the feature selection methodology in Example 1 based on MSKCC data, all significant PSRs were grouped into categories (e.g., Intronic, Intergenic, Antisense, ncTranscript and Promoter Region). In order to assess the prognostic significance of the PSRs differentially expressed within the categories, we developed a k-nearest neighbour (KNN) classifier for each group based on the top 156 PSRs (k=1, correlation distance), trained using features from the comparison of normal and metastatic tissue types (see Example 1 methods). Next, we used unmatched primary tumors (e.g. removing those tumors that had a matched normal in the training subset) as an independent validation set for each KNN classifier. Each primary tumor in the validation set was classified by each KNN as either more similar to normal or metastatic tissue (FIG. 9). Kaplan-Meier analysis of the two groups of primary tumor samples classified by KNN using the biochemical recurrence (BCR) end point was done for KNN classifiers derived for each subset of features. As expected, primary tumors classified by KNN as belonging to the metastasis group had a higher rate of BCR.

Example 11: Genomic Signature of Coding and Non-Coding Features to Predict Outcome after Radical Cystectomy for Bladder Cancer

Methods

251 muscle invasive bladder cancer specimens from University of Southern California/Norris Cancer Center were obtained from patients undergoing radical cystectomies with extended pelvic lymph node dissection between years 1998 and 2004. Archived FFPE specimens sampled corresponded to 0.6 mm punch cores and had a median block age of 13 years. For patients, median follow up was 5 years, median age was 68 years old and the event rate corresponds to 109 patients with progression (43%).

Total RNA was extracted and purified using a modified protocol for the commercially available Agencourt Formapure kit (Beckman Coulter, Indianapolis Ind.). RNA concentrations were determined using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). Purified total RNA was subjected to whole-transcriptome amplification using the WT-Ovation FFPE system according to the manufacturer's recommendation with minor modifications (NuGen, San Carlos, Calif.) and hybridized to Human Exon 1.0 ST GeneChips (Affymetrix, Santa Clara, Calif.) that profiled coding and non-coding regions of the transcriptome using approximately 1.4 million probe selection regions (or PSRs, also referred to as features).

Samples showing a variation of higher than two standard deviation for their average intensities, average background, Relative Log Expression and Median Absolute Deviation were discarded. In addition, filtering was also performed using GNUSE (Global Normalized Unscaled Standard Error), positive versus negative AUC and Percentage of Detected Calls using [0.6,1.4], >0.6 and 20% as thresholds, respectively.

A multivariate outlier detection algorithm was run using the QC metrics provided by Affymetix Power tools available at the world wide web at affymetrix.com/partners_programs/programs/developer/tools/powertools.affx. Samples identified as outliers were also discarded.

The normalization and summarization of the microarray samples were performed with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Results

Table 14 shows the raw clinical data, QC results and classifier scores for each of the 251 samples. The characteristics of the study population is summarized in Table 10. Assessment of the prognostic potential of the clinical factors was assessed by multivariable Cox proportional hazards modeling. As shown in Table 11, Tumor Stage (p=0.04) and Lymph Nodes (p<0.001) were found to have statistically significant prognostic potential based on hazard ratios. In order to assess the discriminatory potential of the clinical and pathological factors, samples were divided into a training set (trn) and a testing set (tst) (see Table 14, ‘Set’ column) and the performance of each variable was assessed by AUC (Table 12) for the progression-free survival endpoint. Progression was defined as any measurable local, regional or systemic disease on post-cystectomy imaging studies.

In agreement with the multivariable analysis, Tumor Stage and Lymph Nodes status had significant performance with a respective AUC of 0.62 and 0.66 for the training set and AUCs of 0.66 and 0.65 for the testing set. Combination of clinical-pathological variables into a multivariate model by either Cox modeling or Logistic Regression resulted in an improved performance (AUCs of 0.72 and 0.71 in the testing set, respectively) compared to these variables as sole classifiers (Table 12).

A genomic classifier (GC) was built based on the Human Exon arrays as follows. First, a ranking of the features by Median Fold Difference (MFD) was generated. Then, a k-nearest neighbour algorithm was applied to an increasingly larger set of features from 10 to 155 based on the MFD ranking. The classifiers (herein referred to as KNN89) were constructed by setting k=21 and number of features=89, achieving an AUC of 0.70 for the training set (FIG. 21A) and an AUC of 0.77 for the testing set (FIG. 21B) based on survival ROC curves at 4 years. The probability, which ranges from 0 to 1, an individual would be classified as having a progression event was based on the expression values of the closest 21 patients in the training cohort of muscle-invasive bladder cancer samples. Low probabilities represent a lower chance a patient would have progression while higher probabilities represent a higher chance a patient would have progression event. The 89 individual features (a.k.a. PSRs) of the KNN89 classifier correspond to coding and non-coding regions of the genome (Table 6, SEQ ID NOs.: 353-441, Table 15) including introns, untranslated regions (or UTRs), features antisense to a given gene as well as intergenic regions. Assessment of the pathways associated to the overlapping genes using KEGG pathway annotation shows that the most represented correspond to Regulation of actin cytoskeleton, focal adhesion and RNA transport (www.genome.jp/kegg/pathway.html).

When combining the GC with the clinical variables Age, Lymphovascular Invasion, Lymph Node Involvement and Intravesical therapy, a new classifier (hereafter referred to as GCC, for Genomic-Clinical Classifier) with enhanced performance was generated, based on the AUC of 0.82 and 0.81 in the training set and testing set respectively (FIG. 21A, FIG. 21B) based on survival ROC curves at 4 years. Discrimination plots for both GC and GCC demonstrated that the separation between the two groups of progression and non-progression samples was statistically significant for both classifiers (FIG. 22). Whereas both calibration plots for GC and GCC showed a good estimation with respect to the true values (FIG. 23), the enhanced performance of the GCC classifier became evident when inspecting the calibration plots, as GCC corrected overestimation of probabilities above 0.5. Still, multivariable analysis of the GC showed that this classifier has unique prognostic potential for the prediction of disease progression after radical cystectomy when adjusted for clinical pathological variables (Table 13).

Cumulative incidence plots depicting the frequency of progression over time were generated for GC-low and GC-high risk groups, as well as for GCC-low and GCC-high risk groups (FIG. 24). The cumulative incidence probabilities of progression were significantly different between the two risk groups for both classifiers. In the case of GC, a 15% incidence for the GC-low risk group was obtained, compared to a 60% incidence for the GC-high risk group at 3 years after radical cystectomy. For the GCC, a 20% incidence of progression for the GCC-low risk group was obtained, compared to a 70% incidence for the GCC-high risk group at 3 years. The 3-fold to 4-fold difference in incidence observed between the low and high risk groups for GC and GCC illustrates the clinical significance of these classifiers.

Example 12: Genomic Signatures of Varying Number of Coding and Non-Coding Features to Predict Outcome after Radical Cystectomy for Bladder Cancer

Methods

251 muscle invasive bladder cancer specimens from University of Southern California/Norris Cancer Center were obtained from patients undergoing radical cystectomies with extended pelvic lymph node dissection between years 1998 and 2004. Archived FFPE specimens sampled correspond to 0.6 mm punch cores and have a median block age of 13 years. For patients, median follow up was 5 years, median age was 68 years and the event rate corresponds to 109 patients with progression (43%).

Total RNA was extracted and purified using a modified protocol for the commercially available Agencourt Formapure kit (Beckman Coulter, Indianapolis Ind.). RNA concentrations were determined using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). Purified total RNA was subjected to whole-transcriptome amplification using the WT-Ovation FFPE system according to the manufacturer's recommendation with minor modifications (NuGen, San Carlos, Calif.) and hybridized to Human Exon 1.0 ST GeneChips (Affymetrix, Santa Clara, Calif.) that profiles coding and non-coding regions of the transcriptome using approximately 1.4 million probe selection regions (or PSRs, also referred to as features).

Samples showing a variation higher than two standard deviation for their average intensities, average background, Relative Log Expression and Median Absolute Deviation were discarded. In addition, filtering was also performed using GNUSE (Global Normalized Unscaled Standard Error), positive versus negative AUC and Percentage of Detected Calls using [0.6,1.4], >0.6 and 20% as thresholds, respectively.

Finally, a multivariate outlier detection algorithm was run using the QC metrics provided by Affymetix Power tools available at the world wide web at affymetrix.com/partners_programs/programs/developer/tools/powertools.affx.

Samples identified as outliers were also discarded.

The normalization and summarization of the microarray samples was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

The dataset was separated into a training (trn) and a testing set (tst) as specified in column ‘Set’ of Table 14. Based on this separation, several machine learning algorithms were trained with different number of features (See Table 16 for methods used for feature selection) and their performance assessed on both training and testing sets independently. Performance of the generated classifiers on the training and the testing set based on AUC was also in Table 16.

Results

FIG. 26 shows the performance of a classifier, NB20, based on 20 features that were a combination of coding, intronic, intergenic, UTR and antisense regions (Table 17). The probability, which ranges from 0 to 1, an individual would be classified as having a progression event was based on the combined proportion of the progression samples in the training cohort which have similar expression values. Low probabilities represent a lower chance a patient would have progression while higher probabilities represent a higher chance a patient would have progression. This classifier had an AUC of 0.81 on the training set (trn) and an AUC of 0.73 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 26A). In order to assess the clinical significance of the classification, after splitting the NB20 classifier scores into two groups by Partitioning Around Medoids (PAM) clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 26B).

FIG. 27 shows the performance of a classifier, KNN12, based on 12 features that were a combination of coding, intronic, intergenic, UTR and antisense regions (Table 17). The probability, which ranges from 0 to 1, an individual would be classified as having a progression event was based on the expression values of the closest 51 patients in the training cohort of muscle-invasive bladder cancer samples. Low probabilities represent a lower chance a patient would have progression while higher probabilities represent a higher chance a patient would have progression. This classifier had an AUC of 0.72 on the training set and an AUC of 0.73 on the testing set, with both AUCs being statistically significant based on Wilcoxon test (FIG. 27A). In order to assess the clinical significance of the classification, after splitting the KNN12 classifier scores into two groups by PAM clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 27B).

FIG. 28 shows the performance of a classifier, GLM2, based on 2 features that corresponded to a pseudogene (HNRNPA3P1) and the intronic region of a protein-coding gene (MECOM) (Table 17). The probability an individual would be classified as having a progression event was based on the best fit expression profile of the training samples. The probabilities range from 0 to 1, where low probabilities represent a lower chance a patient would have progression while high probabilities represent a higher chance a patient would have progression. This classifier had an AUC of 0.77 on the training set and an AUC of 0.74 on the testing set, with both AUCs being statistically significant based on Wilcoxon test (FIG. 28A). In order to assess the clinical significance of the classification, after splitting the GLM2 classifier scores into two groups by PAM clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 28B).

FIG. 29 shows the performance of a single probe selection region corresponding to probe set ID 2704702 that corresponded to the intronic region of a protein-coding gene (MECOM) (Table 17). This classifier had an AUC of 0.69 on the training set and an AUC of 0.71 on the testing set, with both AUCs being statistically significant based on Wilcoxon test (FIG. 29A). In order to assess the clinical significance of the classification, after splitting this classifier scores into two groups by PAM clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 29B).

Example 13: Genomic Signatures of Varying Number of Coding and Non-Coding Features to Predict Gleason Score of 6 Versus Gleason Score Greater than or Equal to 7

Methods

The publicly available expression profiles of normal and prostate tumor samples from the Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor B S, et al., 2010, Cancer Cell, 18:11-22) was downloaded from at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034. Information on Tissue samples, RNA extraction, RNA amplification and hybridization can be found in Taylor B S et al. (2010, Cancer Cell, 18:11-22). The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was performed with the frozen Robust Multiarray Average (IRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:242-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

With the goal of generating classifiers that segregated between samples of Gleason Score of 6 (GS6) versus those with GS greater than or equal to 7 (GS7+), the complete dataset was split into a training set (60%, 78 samples) and a testing set (40%, 52 samples). In the training set, 25 samples were GS6 versus 53 samples that were GS7+. In the testing set, 16 samples were GS6 versus 36 samples that were GS7+.

Based on this separation, several machine learning algorithms were trained with different number of features (see Table 18 for methods used for feature selection) and their performance assessed on both training (trn) and testing (tst) sets independently. Performance of the generated classifiers on the training and the testing set based on AUC was also in Table 18.

Results

FIG. 30 shows the performance of a classifier, SVM20, based on 20 features that were a combination of coding, non-coding transcript, intronic, intergenic and UTR (Table 19). The certainty in which an individual would be classified as having a pathological Gleason grade 4 or higher in their profiled tumor sample was based on the expression values of the top 20 features as ranked by AUC. The GC scores range from negative infinity to positive infinity. Larger values indicate the likelihood that the sample has a pathological Gleason grade of 4 or higher in their profiled tumor sample while smaller values indicate the likelihood that the sample has a pathological Gleason grade of 3 in their profiled tumor sample. This classifier had an AUC of 0.96 on the training set (trn) and an AUC of 0.8 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 30A). The fact that notches within box-plots representing 95% confidence intervals of the SVM20 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 30B) shows that the segregation generated by this classifier was statistically significant.

FIG. 31 shows the performance of a classifier, SVM11, based on 11 features that were a combination of coding, non-coding transcript, intronic, intergenic and UTR (Table 19). The certainty in which an individual would be classified as having a pathological Gleason grade 4 or higher in their profiled tumor sample was based on the expression values of the top 11 features ranked by AUC. The GC scores range from negative infinity to positive infinity. Larger values indicate the likelihood that the sample has a pathological Gleason grade of 4 or higher in their profiled tumor sample while smaller values indicate the likelihood that the sample has a pathological Gleason grade of 3 in their profiled tumor sample. This classifier had an AUC of 0.96 on the training set (trn) and an AUC of 0.8 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 31A). The fact that notches within box-plots representing 95% confidence intervals of the SVM11 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 31B) shows that the segregation generated by this classifier was statistically significant.

FIG. 32 shows the performance of a classifier, SVM5, based on 5 features that were a combination of coding and intronic (Table 19). The certainty in which an individual would be classified as having a pathological gleason grade 4 or higher in their profiled tumor sample was based on the expression values of the top 5 features ranked by AUC. The GC scores range from negative infinity to positive infinity. Larger values indicate the likelihood the sample has a pathological gleason grade of 4 or higher in their profiled tumor sample while smaller values indicate the likelihood the sample has a pathological gleason grade of 3 in their profiled tumor sample. This classifier had an AUC of 0.98 on the training set (trn) and an AUC of 0.78 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 32A). The fact that notches within box-plots representing 95% confidence intervals of the SVM5 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 32B) shows that the segregation generated by this classifier was statistically significant.

FIG. 33 shows the performance of a classifier, GLM2, based on 2 features, one of them being intronic to gene STXBP6 and the other corresponding to an intergenic region (Table 19). The probability an individual would be classified as having a pathological gleason grade 4 or higher in their profiled tumor sample was based on the best fit expression profile of the training samples. The probabilities range from 0 to 1 where low probabilities represent a lower chance the pathological gleason grade of the profiled tumor is 4 or higher while high probabilities represent a higher chance the pathological gleason grade of the profiled tumor is 4 or higher. This classifier had an AUC of 0.86 on the training set (trn) and an AUC of 0.79 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 33A). The fact that notches within box-plots representing 95% confidence intervals of the GLM2 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 33B) shows that the segregation generated by this classifier was statistically significant.

Example 14: Prognostic Potential of Inter-Correlated Expression (ICE) Blocks with Varying Composition of Coding and Non-Coding RNA

Methods

The publicly available expression profiles of normal and prostate tumor samples, Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor B S, et al., 2010, Cancer Cell, 18:11-22) were downloaded from at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034.

The Human Exon arrays for 131 primary prostate cancer, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at the world wide web at ncbi.nlm.nih.gov/geo/series GSE21034. Information on Tissue samples, clinical characteristics, RNA extraction, RNA amplification and hybridization can be found as described in Taylor B S, et al., (2010, Cancer Cell, 18:11-22). The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was performed with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:242-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Annotation of PSRs

Using annotation data from the human genome version hg19/GRCh37 (Ensembl annotation release 62) and xmapcore (Yates, 2007), we categorized the PSRs depending on the chromosomal location and orientation with respect to coding and non-coding gene annotation as Coding, Non-coding (UTR), Non-coding (ncTranscript), Non-coding (Intronic), Non-coding (CDS_Antisense), Non-coding (UTR_Antisense), Non-coding (ncTranscript_Antisense), Non-coding (Intronic_Antisense), Non-coding (Intergenic).

Definition of Inter-Correlated Expression (ICE) Blocks

Affymetrix Human Exon ST 1.0 Arrays provide ˜5.6 million probes which were grouped into ˜1.4 million probe sets (average of 4 probes per probe set). The expression value captured for each probe was summarized for each probe set. The PSRs corresponding to each probe set fell within coding and non-coding (introns, UTRs) regions of protein-coding and non-protein-coding genes, as well as antisense to genes and intergenic regions.

An additional level of summarization provided by Affymetrix corresponds to probe sets that were grouped into so called transcript clusters. The genomic location of transcript clusters was defined based on the annotation of gene structures from multiple sources. The probe sets that compose these transcript clusters usually correspond to coding segments of protein-coding genes. This summarization was done with the goal of representing into one value the expression of the gene.

The predefined Affymetrix transcript clusters have a number of drawbacks including (i) they were static definitions of the transcribed sequence for a given gene, (ii) they do not account for the expression levels of the samples being assessed, and hence might correspond to sub-optimal representations of the expressed unit. Additionally, novel types of transcribed sequences that challenge the standard exon/intron structure of a gene such as chimeric RNAs (Kannan et al 2011) and very long intergenic non-coding regions (or vlincs, Kapranov et al 2010) have been found to be differentially expressed in cancer, and hence approaches that detect such transcripts were needed.

We proposed a new method that found blocks of neighboring correlated PSRs based on their expression values and show that they have prognostic potential. The correlated expression of these blocks of PSRs should represent one or more molecules that were being transcribed as either a single unit (e.g. chimeric RNAs) or as separate units (e.g. two separate genes) through cancer progression. We call these blocks syntenic blocks or Inter-Correlated Expression (ICE) Blocks.

Given a pooled set of samples from two groups A and B (e.g. primary tumor tissue versus metastatic tumor tissue) a window size W measured in number of PSRs, a correlation threshold T between 0 and 1, a counter C set to 0 and the chromosome, chromosomal location and strand for each PSR, ICE blocks were computed as follows:

-   -   1) Define the first block L as the single first PSR in the first         chromosome.     -   2) Measure its correlation to the immediate adjacent PSR P         downstream on the same strand using Pearson's correlation         metric.     -   3) If the correlation was greater or equal than T, then merge P         to block L. If not, then skip P and add one to counter C.     -   4) Repeat steps 1)-3) using the right-most PSR of block L. If a         new PSR was added to the block, reset C=0.     -   5) Return block L when C>W or when reached the last PSR within         the chromosome. Set C=0.     -   6) Repeat 1)-4) for each strand of each chromosome.

Once the ICE blocks were defined, the expression values for each of them were summarized based on the median value of the expression associated to the PSRs that compose the ICE Block for each patient. The significance of the differential expression between groups A and B for block L was assessed by computation of a Wilcoxon test P-value.

Results

Given the publicly available MSKCC samples described in Methods, the following comparisons were pursued: (i) Normal Adjacent Tissue versus Primary Tumor, (ii) Primary Tumor versus Metastatic Tissue, (iii) Gleason Score >=7 versus Gleason Score <7 and (iv) Biochemical Recurrence (BCR) vs non-BCR.

The algorithm for ICE block detection was applied to each of the pairwise comparisons. The number of ICE blocks found for each comparison and for a number of different Pearson correlation thresholds is shown in Table 20. As expected, as the correlation threshold gets lower more ICE blocks were found, consistent with the idea that more adjacent PSRs can be merged with lower correlation thresholds. Also shown in Table 20 is the number of ICE blocks found to be significantly differentially expressed (P-value<0.05) between the two conditions for each pairwise comparison. For those comparisons involving different progression states of cancer, the number of ICE blocks found differentially expressed can range from several hundreds (e.g. BCR endpoint with correlation threshold of 0.9) to tens of thousands (e.g. Primary vs Metastasis comparison, correlation threshold of 0.6).

Since ICE Blocks were composed of two or more PSRs, the proportion of coding and non-coding regions that the ICE block consists of can vary depending on where the associated PSRs fell into. Table 21 shows, for different comparisons and correlation thresholds, the frequency of ICE blocks found differentially expressed that correspond to a number of compositions including those that were composed only of coding regions, only intronic regions, only intergenic regions, only antisense regions as well as all other combinations. Additionally, ICE blocks can overlap with two or more adjacent genes (Multigene column in Table 21), suggesting that the two units were being differentially co-expressed either as separate units or as chimeric RNAs. For example, for the BCR endpoint and correlation threshold of 0.8, a previously reported chimeric RNA consisting of genes JAM3 and NCAPD3 was found as an ICE block composed of 65 coding and non-coding PSRs across the genomic span chr11:134018438 . . . 134095174;—with statistically significant differential expression (P-value<0.04).

Table 22 provides a list of all those ICE blocks found differentially expressed for the Gleason Score comparison when using a strict correlation threshold of 0.9. Table 23 provides a list of all those ICE blocks found differentially expressed for the Biochemical Recurrence endpoint when using a strict correlation threshold of 0.9. For each block, the associated P-value that demonstrated the differential expression (p<0.05), the PSRs included within the block, the percentage composition of coding and non-coding as well as the overlapping gene(s) within the same chromosomal location were shown. As seen in Tables 22 and 23, the proportion of coding and non-coding PSRs that an ICE block can be composed of can vary from fully coding to fully non-coding, with multiple proportions in between.

In order to further illustrate the discriminatory ability of these ICE blocks, FIGS. 34-39 show the box-plots (A) and ROC curves (B) for five different ICE blocks (FIG. 34: Block_7716, FIG. 35: Block_4271, FIG. 36: Block_5000, FIG. 37: Block_2922 and FIG. 38: Block_5080) of varying composition of coding and non-coding found to be differentially expressed in GS6 vs GS7+ comparison (Table 22, see Table 24 for sequences associated to each PSR composing these ICE Blocks). For each of these ICE Blocks, box-plots depicting the distribution of the ICE Block expression were displayed for both groups. The fact that notches within box-plots representing 95% confidence intervals of the expression associated to those GS6 samples and GS7+ samples didn't overlap (FIGS. 34A, 35A, 36A, 37A, and 38A) shows that the segregation generated by this classifier was statistically significant. The statistical significance of this segregation was further confirmed by the AUC associated to each of the ROC curves for these ICE Blocks, as the 95% confidence intervals associated to each of the AUCs do not cross the 0.5 lower bound FIGS. 34B, 35B, 36B, 37B and 38B).

FIGS. 39-45 show the box-plots (A), ROC curves (B) and Kaplan-Meier curves (C) for seven different ICE blocks (FIG. 39: Block_6592, FIG. 40: Block_4627, FIG. 41: Block_7113, FIG. 42: Block_5470, FIG. 43: Block_5155, FIG. 44: Block_6371 and FIG. 45: Block_2879) of varying composition of coding and non-coding found to be differentially expressed in BCR versus non-BCR comparison (Table 23, see Table 24 for sequences associated to each PSR composing these ICE Blocks). For each of these ICE Blocks, box-plots depicting the distribution of the ICE block expression were displayed for both groups. The fact that notches within box-plots representing 95% confidence intervals of the expression associated to those GS6 samples and GS7+ samples don't overlap (FIGS. 39A, 40A, 41A, 42A, 43A, 44A, and 45A) shows that the segregation generated by this classifier was statistically significant. The statistical significance of this segregation was further confirmed by the AUC associated to each of the ROC curves for these ICE blocks, as the 95% confidence intervals associated to each of the AUCs do not cross the 0.5 lower bound (FIGS. 39B, 40B, 41B, 42B, 43B, 44B, and 45B). In order to assess the clinical significance of the classification, after splitting the ICE blocks scores into two groups by median split method, Kaplan-Meier curves show that the two groups represent significantly different groups of high-risk of BCR vs low-risk of BCR (FIGS. 39C, 40C, 41C, 42C, 43C, 44C, and 45C).

Example 15: KNN Models for Tumor Upgrading

Methods

Although pure GG3 (i.e. Gleason 3+3) was rarely lethal, some GG3 cancers were associated with clinically metastatic disease. In this example, a signature was developed based on post-RP prostate tumor samples to identify which have transitioned from low risk, as defined by biopsy GS 6, clinical stage either T1 or T2A, and pretreatment PSA≤10 ng/ml, to high risk tumors, as defined by a pathological GS≥7 or a pathological tumor stage >T3A.

The publically available Memorial Sloan Kettering (MSKCC) Prostate Oncogenome project dataset (at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034) was used for this analysis, which consisted of 131 primary tumor microarray samples (Affymetrix Human Exon 1.0 ST array). Information on Tissue samples, RNA extraction, RNA amplification and hybridization can be found as found in, for example, Taylor B S, et al. (2010, Cancer Cell, 18:11-22). These samples were preprocessed using frozen Robust Multiarray Average (IRMA), with quantile normalization and robust weighted average summarization (see McCall M N, et al., 2010, Biostatistics, 11:242-53 McCall M N, et al., 2011, Bioinformatics, 12:369). Of these patients, 56 net the low risk specification defined above. These patient samples were randomly partitioned into a training (n=29) and testing set (n=27) in a manner which ensures the number of cases and controls remained proportional (Table 25).

The 1,411,399 expression features on the array were filtered to remove unreliable probe sets using a cross hybridization and background filter. The cross hybridization filter removes any probe sets which were defined by Affymetrix to have cross hybridization potential (class 1), which ensures that the probe set was measuring only the expression level of only a specific genomic location, Background. filtering removes features with expression levels lower than the median expression level of the background probe sets. These filters reduced the number of features to 891,185. The training set was further processed using median fold difference (MFD>1.4) filter to 157 genomic features then ranked by T-Test P-value. The top 16 features (Table 26) of the training set were used for modeling a KNN classifier (k=3, Euclidean distance).

Results

The KNN model (hereafter called KNN16) was applied to the testing set and analyzed for its ability to distinguish tumors which underwent upgrading from those that remained low risk (FIG. 46). The KNN16 score, which ranges from 0 to 1, is the percentage of the 3 closest training set patients which upgraded as defined by biopsy (Gleason <6, PSA≤10 ng/ml, clinical stage T1 or T2A) transitioning to a higher risk tumor following RP (pathological GS≥7 or a pathological tumor stage >T3A). The higher the KNN16 score, the more likely the patient will experience an upgrading event. As depicted by the non-overlap of the notches for the discrimination plots for both groups (FIG. 46), the low-risk and upgraded groups were significantly different. Additionally, KNN16 (AUC=0.93) had a better ability to discriminate upgraded patients compared to the clinical factors: pretreatment PSA (preTxPSA, AUC=0.52), clinical tumor stage (c1 Stage, AUC=0.63), and patient age (AUC=0.56) (FIG. 47). In terms of accuracy, the model performed with an accuracy of 81% (P-value <0.005) over an accuracy of 56%, achieved by labeling all samples with the majority class (null model).

In order to assess how the expression profiles group, clustering analysis was also performed for the pooled samples from training and testing sets (n=56) (FIG. 48). The 157 genomic features were subjected to a T-Test filter (P-value <0.05) resulting in 98 features. The two distinct clusters observed, one mostly corresponding to samples which had upgrading and the other corresponding mostly to low risk samples, confirm the ability of the selected features to discriminate between low-risk and upgraded samples.

The results based on this signature show that the selected markers have the potential to provide more accurate risk stratification than predictive models based only on clinical parameters, and identify patients who should consider definitive local therapy rather than AS.

Example 16: Non-Coding RNAs Differentially Expressed Through Lung and Colorectal Cancer

Data Sets and Methodology

Lung Samples

The cohort contains 40 samples corresponding to 20 tumor samples and their paired normal tissue. Methodology on the generation and processing of samples was disclosed in Xi L et al (2008, Nucleic Acids Res, 36:6535-47). Files with raw expression values for each sample were publicly available at the world wide web at ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE12236.

Colorectal Samples

The cohort contains 173 samples, 160 of which correspond to tumor and the remaining 13 correspond to normal colonic mucosa biopsy. Methodology on the generation and processing of samples was disclosed in Sveen A, et al. (2011, Genome Med, 3:32). Files with raw expression values for each sample were publicly available at the world wide web at ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE24551.

Normalization and Summarization

Dataset normalization and summarization was performed with fRMA (McCall M N, et al., 2010, Biostatistics, 11:242-53). The fRMA algorithm relates to the RMA (Irizarry R A, et al., 2003, Biostatistics, 4:249-64) with the exception that it specifically attempts to consider batch effect during data summarization and was capable of storing the model parameters in so called frozen vectors. fRMA then uses these frozen vectors to normalize and summarize raw expression probes into so-called probes selection regions (PSRs) in log 2 scale. The frozen vectors negate the need to reprocess the entire data set when new data was received in the future. For both colorectal and lung samples, batches were defined based on the date used to measure the expression on the samples as provided in the raw data. In the case of lung samples, a custom set of frozen vectors was generated by randomly selecting 6 arrays from each of 4 batches in the data set; one batch was discarded from the vector creation due to the small number of samples in that batch (McCall M N, et al., 2011, Bioinformatics, 12:369). For the colorectal samples, a custom set of frozen vectors was generated by randomly selecting 4 arrays from each of 24 batches in the data set. Seventeen batches were discarded from the vector creation due to the small number of samples (McCall M N, et al., 2011, Bioinformatics, 12:369).

Filtering

Cross hybridization and background filtration methods were applied to all PSRs on the array in order to remove poorly behaving PSRs. Two sources of cross-hybridization were used for filtering: (i) probe sets defined as cross-hybridizing by affymetrix (at the world wide web at affymetrix.com) and (ii) probe sets defined as “unreliable” by the xmapcore R package (at the world wide web at xmap.picr.man.ac.uk). The cross hybridization filters reduce the number of PSRs in the analysis from 1,432,150 to 1,109,740.

PSRs with associated expression levels at or below the chip's background expression level did not contain reliable expression information. The background expression of the chip was calculated by taking the median of the linear scale expression values of the 45 anti-genomic background PSRs (Affymetrix Technical Note, 2011). For any type of comparison (e.g. normal tissue versus tumor), if the median expression of both groups was less than the background expression level, then the PSR was removed from further analysis. It should be made clear that, if the expression level for a PSR tended to be above the background threshold in one group but not the other, the PSR remained in the analysis as this could be a sign of a genuine biological difference between the two groups.

Unsupervised Analysis

A PSR was defined as differentially expressed between two groups if the median fold difference was greater or equal than 1.5. For those PSRs complying to that threshold, assessment of the ability to segregate between two groups was done using multidimensional scaling (MDS). MDS plots were shown to visualize the differences between the marker expression levels of two groups in three dimensions. The Pearson distance metric was used in these MDS plots, and the permanova test was used to assess the significance of the segregation (at the world wide web at cran.r-project.org/web/packages/vegan/index.html).

Annotation of Probe Sets (PSRs)

Using annotation data from the human genome version hg19/GRCh37 (Ensembl annotation release 62) and xmapcore (Yates, 2007), we categorized the PSRs depending on the chromosomal location and orientation with respect to coding and non-coding gene annotation as Coding, Non-coding (UTR), Non-coding (ncTranscript), Non-coding (Intronic), Non-coding (CDS_Antisense), Non-coding (UTR_Antisense), Non-coding (ncTranscript_Antisense), Non-coding (Intronic_Antisense), Non-coding (Intergenic).

Ontology Enrichment Analysis

DAVID Bioinformatics tool was used to assess enrichment of ontology terms (Huang da W, et al., 2009, Nat Protoc, 4:44-57; Huang da W, et al., 2009, Nucleic Acids Res, 37:1-13)

Results

Non-Coding RNAs Differentially Expressed Between Normal Tissue and Lung Cancer

Based on the methodology described above, and after filtering 480,135 PSRs because of low expression values compared to background (17.18 threshold), the differential expression of all remaining PSRs was tested. 3,449 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 27 provides the top 80 non-coding PSRs). Of these, 1,718 PSRs (˜50%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). Furthermore, ˜35% of the PSRs (1,209/3,449) fall within non-coding parts of a protein-coding gene such as UTRs and introns.

Additionally, ^(˜)4% of the PSRs were found to overlap with 202 transcripts that did not encode for a functional protein. The distribution of these non-coding transcripts, according to Ensembl annotation (at the world wide web at ensembl.org), were as follows: 79 “processed transcript”, 43 “retained intron”, 32 “large intergenic non-coding RNA”, 23 “antisense”, 11 “pseudogene”, 10 “non-sense mediated decay”, 2 “non_coding”, 1 “sense intronic” and 1 “miRNA”.

Most of the PSRs were found within the boundaries of a gene, with only ˜6% of PSRs (207/3449) being intergenic. In total, 1,205 genes were found to overlap with the PSRs. Ontology enrichment analysis of the genes corrected for multiple testing shows multiple cellular processes expected to be found significantly enriched in the differentiation between normal adjacent and tumor tissues, including cell division, cell adhesion and regulation for muscle development.

The utility of the differentially expressed non-coding features can be seen from their ability to separate normal versus tumor cancer samples using unsupervised techniques (FIG. 49A). The multidimensional scaling (MDS) plot shows that these non-coding features generate a clear segregation between the normal samples and the matched tumor samples; the segregation was found to be statistically significant (p<0.001).

Non-Coding RNAs Differentially Expressed Between Normal Tissue and Colorectal Cancer

Based on the methodology described above, and after filtering 672,236 PSRs because of low expression values compared to background (33.3 threshold), the differential expression of all remaining PSRs was tested. 4,204 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 28 provides the top 80 non-coding PSRs). Of these, 2,949 PSRs (˜70%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). Furthermore, ˜55% of the PSRs (2,354/4,204) fall within non-coding parts of a protein-coding gene such as UTRs and introns.

Additionally, ^(˜)8% of the PSRs were found to overlap with 368 transcripts that did not encode for a functional protein. The distribution of these non-coding transcripts distribute, according to Ensembl annotation (at the world wide web at ensembl.org), were as follows: 143 “processed transcript”, 141 “retained intron”, 26 “large intergenic non-coding RNA”, 25 “non-sense mediated decay”, 18 “pseudogene”, 9 “antisense”, 2 “sense intronic”, 2 “miscRNA”, 1 “snRNA” and 1 “non_coding”.

Most of the PSRs were found within the boundaries of a gene, with only ˜5% of the PSRs (209/4204) being intergenic. In total, 1,650 genes were found to overlap with the PSRs. Ontology enrichment analysis of the genes corrected for multiple testing shows cell adhesion, collagen metabolism and catabolism to be significantly enriched in the differentiation between normal adjacent and tumor tissues; the differential expression of features associated to collagen processes was in agreement with previous studies in colorectal carcinogenesis (Skovbjerg H, et al., 2009, BMC Cancer, 9:136).

The utility of the differentially expressed non-coding features can be seen from their ability to separate normal versus tumor cancer samples using unsupervised techniques (FIG. 49B). The multidimensional scaling (MDS) plot shows that these non-coding features generate a clear segregation between the normal and tumor samples; the segregation was found to be statistically significant (p<0.001).

Non-Coding RNAs Differentially Expressed Between Different Stages of Lung Cancer

Based on the methodology described above, the ability of non-coding RNAs to discriminate between two groups of lung tumor tissues was explored. In particular, the non-coding RNAs were inspected for their discriminatory ability between early stage lung cancer (12 stage I samples) versus more advanced stages of cancer (3 stage II patients and 5 stage III patients, collectively called the II+III group). After filtering 477,912 PSRs because of low expression values compared to background (17.18 threshold), the differential expression of all remaining PSRs was tested. 618 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 29 provides the top 80 non-coding PSRs). Of these, 439 PSRs (71%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). Furthermore, ˜38% of the PSRs (235/618) fell within non-coding parts of a protein-coding gene such as UTRs and introns.

Additionally, ^(˜)11% of the PSRs were found to overlap with 67 transcripts that did not encode for a functional protein. The distribution of these non-coding transcripts distribute, according to Ensembl annotation (at the world wide web at ensembl.org), were as follows: 19 “processed transcript”, 11 “retained intron”, 9 “large intergenic non-coding RNA”, 15 “pseudogene”, 6 “non-sense mediated decay”, 3 “antisense”, 1 “misc RNA”, 1 “retrotransposed” and 1 “miRNA”.

Most of the PSRs were found within the boundaries of a gene; however, approximately 17% of the PSRs (104/618) fell in intergenic regions. In total, 472 genes were found to overlap with the PSRs. Ontology and pathway enrichment analysis of the genes corrected for multiple testing shows no processes or pathways found to be significantly enriched in the differentiation between tumor stages. Given that most of the differentially expressed features were of non-coding nature, and as enrichment analyses greatly rely on the annotation of protein-coding genes, these results suggest that further functional studies on non-coding RNAs were critical for understanding the biology that was involved in the progression of lung cancer.

The utility of the differentially expressed non-coding features can be seen from their ability to separate tumor stage I versus II+III cancer samples using unsupervised techniques (FIG. 50A). The multidimensional scaling (MDS) plot shows that these non-coding features generate a better segregation between different stages than coding features; the segregation was found to be statistically significant (p<0.001).

XIST Non-Coding RNA was Differentially Expressed Between Stages II and III of Colorectal Cancer.

The ability of non-coding RNAs to discriminate between two groups of colorectal tumor tissues was explored. In particular, the non-coding RNAs were inspected for their discriminatory ability between stage II (90 samples) and stage III (70 samples) colorectal cancer samples. Based on the methodology described above, and after filtering 703,072 PSRs because of low expression values compared to background (33.3 threshold), the differential expression of all remaining PSRs was tested. 35 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 30 list the non-coding PSRs found with this threshold). Of these, 25 PSRs (71%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). In addition to two of these non-coding PSRs falling within the UTRs of protein-coding genes DDX3Y (DEAD (Asp-Glu-Ala-Asp) box polypeptide 3) and KDM5D (lysine (K)-specific demethylase 5D), both Y-linked, the remaining 23 differentially expressed non-coding PSRs correspond to the X-inactive-specific transcript (XIST), a long non-coding RNA gene residing in the X chromosome that plays an essential role in X-chromosome inactivation (Brown C J, 1991, Nature, 349:38-44). FIG. 50B illustrates the density of a PSR representative of XIST. As seen there, stage II samples tend to have low expression values whereas stage III samples tend to have high expression values of XIST, suggesting that this gene gets overexpressed through colorectal cancer progression. Highly variable expression of this lncRNA has been detected within BRCA1 primary tumors in breast cancer (Vincent-Salomon A, et al., 2007, Cancer Res, 67:5134-40); a recent study shows that XIST presents DNA copy-number variations in microsatellite-unstable sporadic colorectal carcinomas, a particular type of tumor generally regarded as diploid (Lassman S, et al., 2007, J Mol Med (Berl), 85:293-304). Interestingly, 38 of the 160 colorectal tumor samples used for this example correspond to microsatellite-unstable colorectal carcinomas. These suggest that the DNA copy-number variation that involves XIST might have an impact on the dosage of the gene at the transcript level that was detected in this analysis due to the inclusion of microsatellite-unstable tumor samples.

Example 17: Comparison of Genomic Signatures with Coding and Non-Coding Features and Genomic Signatures with Coding Features

The performance of several previously published classifiers can be compared to new classifiers based on the publicly available genomic and clinical data generated by the Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate Oncogenome Project (Taylor et al., 2010) available from GEO Omnibus at the world wide web at ncbi.nlm.nih.gov/geo/series GSE21034. The previously published classifiers are designed for predicting Biochemical recurrence (BCR) or other endpoint that indicates disease progression based solely on coding features. The newly developed classifiers are designed for predicting BCR and are composed of coding and non-coding features. CEL files for the arrays from the dataset are pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon P-test or Median Fold Difference, and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, Random Forest) that generate classifiers. Classifiers can be selected based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The performance of previously published classifiers and the new classifier can be compared by one or more of the metrics disclosed herein. The newly developed classifiers, containing both coding and non-coding features, that outperform the previously published coding classifiers by a statistically significant difference of the metrics disclosed herein, either measured by a P-value threshold of ≤0.05 or non-overlapping confidence intervals for the metric of performance applied can be used in any of the methods, systems, or kits disclosed herein.

Example 18 Generation of Prognostic Genomic Signatures with Coding and Non-Coding Features for Gastric Cancer

Based on the publicly available genomic and clinical data from GEO Omnibus, which can be downloaded at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27342 and at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13195, a newly developed classifier can be created for discriminating different stages of gastric cancer and can be composed of coding and non-coding features. CEL files for the arrays from the dataset can be pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon test or Median Fold Difference (MFD), and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, and Random Forest) that generate classifiers. Selection of the classifiers for gastric cancer can be based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The newly developed classifier, containing both coding and non-coding features, can show prognostic ability as supported by the statistical significance of the metrics applied.

Example 19 Generation of Prognostic Genomic Signatures with Coding and Non-Coding Features for Neuroblastoma

Based on the publicly available genomic and clinical data from GEO Omnibus which can be downloaded at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27608, a newly developed classifier can be created for discriminating different stages of neuroblastoma and can be composed of coding and non-coding features. CEL files for the arrays from the dataset can be pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon test or Median Fold Difference, and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, and Random Forest) that generate classifiers. Selection of the classifier for neuroblastoma can be based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The newly developed classifier for neuroblastoma, containing both coding and non-coding features, can show prognostic ability as supported by the statistical significance of the metrics applied.

Example 20 Generation of Prognostic Genomic Signatures with Coding and Non-Coding Features for Glioma

Based on the publicly available genomic and clinical data from GEO Omnibus, which can be downloaded at the world wide web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30472, a newly developed classifier is created for discriminating different grades of glioma and can be composed of coding and non-coding features. CEL files for the arrays from the dataset can be pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon test or Median Fold Difference, and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, and Random Forest) that generate classifiers. Selection of the classifiers for glioma can be based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The newly developed classifier, containing both coding and non-coding features, can show prognostic ability as supported by the statistical significance of the metrics applied.

TABLE 1 Abbreviation Description AUC Area Under Curve BCR Biochemical Recurrence CM Clinical Model CR Clinical Recurrence ECE Extra Capsular Extensions FFPE Formalin Fixed Paraffin Embedded fRMA Frozen Robust Multiarray Average GC Genomic Classifier GCC Genomic Clinical Classifier IQR Interquartile Range LNI Lymph Node Invasion MDA Mean Decrease in Accuracy MDG Mean Decrease in Gini MSE Mean Squared Error NED No Evidence of Disease OOB Out of Bag (sampling) PCSM Prostate Cancer Specific Mortality PSA Prostate Specific Antigen PSR Probe Selection Region RP Radical Prostatectomy SVI Seminal Vesicle Invasion SMS Surgical Margin Status UTR Untranslated Region

TABLE 2 Primary tumour Metastasis N 131 19 Median age at Dx 58 58 (years) Pre-op PSA (ng/ml) <10 108 7 ≥10 <20 16 1 ≥20 6 9 NA 1 2 Pathological Gleason ≤6 41 0 Score 7 74 2 ≥8 15 7 NA 1 10 Pathological Stage T2 85 1 T3 40 7 T4 6 2 NA 0 9

TABLE 3 Name Definition Processed Non-coding transcript that does not contain an Transcript ORF. Retained Intron Non-coding transcript containing intronic sequence. Non-sense The transcript is thought to go non-sense mediated Mediated Decay decay, a process which detects non-sense mutations (NMD) and prevents the expression of truncated ande erroneous proteins. LincRNA Large Intergenic Non-Coding RNA, or Long non- coding RNA, usually associated with open chroma- tin signatures such as histone modification sites. Antisense Non-coding transcript believed to be an antisense product used in the regulation of the gene to which it belongs. Processed Non-coding Pseudogene produced by integration of Pseudogene a reverse transcribed mRNA into the genome. Unprocessed A non-coding pseudogene arising from gene Pseudogene duplication. Pseudogene A non-coding sequence similar to an active protein MiRNA MicroRNA is single stranded RNA, typically 21- 23 by long, that is thought to be involved in gene regulation (specially inhibition of protein expression) Non Coding Transcript does not result in a protein product Sense Intronic Has a long non-coding transcript in introns of a coding gene that does not overlap any exons (from VEGA definition)

TABLE 4 MFD: Median Fold Difference in this dataset in various comparisons. Probe set Adjusted Gene ID Type Comparison MFD P-value P-value H19 3359088 Intron Metastatic Vs Primary 1.86 <0.3 1 MALAT1 3335167 Exon Normal Vs Primary 1.56 <0.1 1 MALAT1 3335168 Exon Normal Vs Primary 1.73 <0.2 1 MALAT1 3335176 Exon Normal Vs Primary 1.78 <0.05 1 MALAT1 3335179 Exon Normal Vs Primary 1.59 <0.7 1 MALAT1 3335194 Exon Metastatic Vs Primary 0.53 0.000 0.029 MALAT1 3335196 Exon Metastatic Vs Primary 0.63 0.000 0.001 PCA3 3175539 Exon Metastatic Vs Primary 1.50 <0.02 1 PCA3 3175540 Exon Normal Vs Primary 1.90 0.000 1.36E−11 PCA3 3175545 Intron Normal Vs Primary 1.53 0.000 2.33E−09 PCGEM1 2520743 Exon Metastatic Vs Primary 0.63 <0.002 0.05 PCGEM1 2520744 Exon Metastatic Vs Normal 1.53 <0.3 1 PCGEM1 2520744 Exon Normal Vs Primary 0.64 <0.002 0.07 PCGEM1 2520745 Intron Normal Vs Primary 1.52 0.000 0.04 PCGEM1 2520746 Exon Metastatic Vs Normal 1.61 <0.5 1 PCGEM1 2520749 Exon Metastatic Vs Normal 1.55 <0.2 1 PCGEM1 2520749 Exon Metastatic Vs Primary 0.62 0.000 0.01

TABLE 5 SVI: Seminal Vesicle Invasion ECE: Extracapsular Extension, SMS: Surgical Margin Status, LNI: Lymph node Involvement, PreTxPSA: Pre-operative PSA, PGS: Pathological Gleason Score. Classifier Coding Non-Coding Non-Exonic Odd P- Odd P- P- Predictor Ratio value Ratio value Odd Ratio value KNN Positive* 2.49 0.63 15.89  0.14

SVI 0.26 0.42 0.29 0.44  0.52 0.69 SMS 0.64 0.73 1.06 0.97  0.89 0.94 LNI

 

22.7  0.1  55.74 0.09 log2(Pre-Op PSA)

 

ECE

 

225.84  0.06 356.81  0.06 Path Gleason Score

6.48 0.06  6.65 0.07 *KNN Positive: Metastatic-like

TABLE 6 SEQ ID NO. Type Sequence 1 CODING CCTGCCATGTACGTCGCCATTCAAGCTGTGCTCTCCCTCTATGCCTC TGGCCGCACGACA 2 CODING GGCTCAGAGCAAGCGAGGGATCCTAACTCTCAAATACCCCATTGAA CACGGC 3 CODING GGATTCAGGTGATGGCGTCACCCACAATGTCCCCATCTATGAAGGC TATGCCCTGCCCCATGCCATCATGCGCCTGGACTTGGCTGGCCGTG ACCTCACGGACTACCTCATGAAGATCCTCACAGAGAGAGGCTATTC CTTTGTGAC 4 CODING TGAAGGTGGTATCATCGGTCCTGCAGCTT 5 CODING CTGCGTGTAGCACCTGAAGAGCACCCCACCCTGCTCACAGAGGCTC CCCTAAATCCCAAGGCCAACAGGGAAAAGATGACCCAG 6 CODING CATCCGCATCAACTTCGACGTCACGG 7 CODING GCATGGAGTCCGCTGGAATTCATGAGACAACCTACAATTCCATCAT GAAGTGTGACATTGACATCCGTAAGGACTTATATGCCAAC 8 CODING TGCTCAGAAAGTTTGCCACCTCATGGGAATTAATGTGACAGATTTC ACCAGATCCATCCTCACTCCTCGTATCAAGGTTGGGCGAGATGT 9 CODING TTTGGCCAAGGCAACATATGAGCGCCTTTTCCGCTGGATACTCACC CGCGTGAACAAAGCCCTGGACAAGACCCATCGGCAAGGGGCTTCC TTCCTGGGGATCCTGGATATAGCTGGATTT 10 CODING CTATAATGCGAGTGCCTGGCTGACCAAGAATATGGACCCGCTGAAT GACAACGTGACTTCCCTGCTCAATGCCTCCTCCGACAAGTTT 11 CODING AGAGAGAAATTGTGCGAGACATCAAG 12 CODING GGAGGAGTCCCAGCGCATCAACGCCAACCGCAGGAAGCTGCAGCG GGAGCTGGATGAGGCCACGGAGAGCAACGAGGCCATGGGCCGCGA GGTGAACGCACTCAAGAGC 13 CODING ATCGGGAGGACCAGTCCATTCTATGCAC 14 CODING AAGCAGCTTCTACAAGCAAACCCGATTCTGGAGGCTTTCGGCAACG CCAAAACAGTGAAGAACGACAACTCCTCA 15 CODING GGAGGGCTTCAACAACTACACCTTCCTCTCCAATGGCTTTGTGCCC ATCCCAGCAGCCCAGGATGATGAGATGTTCCAGGAAACCGTGGAG GCCATGGCAATCATGGGTTTCAGCGAGGAGGA 16 CODING ACCAGTCAATCAGGGAGTCCCGCCACTTCCAGATAGACTACGATGA GGACGGGAACTGCTCTTTAATTATTAGTGATGTTTGCGGGGATGAC GATGCCAAGTACACC 17 CODING AAGGTCTGGAGGACGTAGAGTTATTGAAAATGCAGATGGTTCTGA GGAGGAAACGGACACTCGAGACGCAGACTTCAATGGAACCAAGGC 18 CODING CCTGGACCAGATGGCCAAGATGACGGAGAGCTCGCTGCCCAGCGC CTCCAAGACCAAGAAGGGCATGTTCCGCACAGTGGGGCAGCTGTA CAAGGAGCAGCTGGGCAAGCTGATGACCACGCTACGCAACACCAC GCCCAACTTC 19 CODING GGAAGATGCCCGTGCCTCCAGAGATGAGATCTTTGCC 20 CODING CTTCACGAGTATGAGACGGAACTGGAAGACGAGCGAAAGCAACGT GCCCTGGC 21 CODING CAAGCTGGATGCGTTCCTGGTGCTGGAGCAGCTGCGGTGCAATGGG GTGCTGGAAGGCATTCGCATCTGCCGGCAGG 22 CODING CTGCTAGAAAAATCACGGGCAATTCGCCAAGCCAGAGAC 23 CODING CACCACGCACACAACTACTACAATTCCGCCTAG 24 CODING CCTGTTCACGGCCTATCTTGGAGTCGGCATGGCAAACTTTATGGCT GAG 25 CODING GCCAAACTGCGGCTGGAAGTCAACATGCAGGCGCTCAAGGGCCAG TTCGAAAGGGATCTCCAAGCCCGGGACGAGCAGAATG 26 CODING GCTGAAACGGAAGCTGGAGGGTGATGCCAGCGACTTCCACGAGCA GATCGCTGACCTCCAGGCGCAGATCGCAGAGCTC 27 CODING CCAGCTGGATGGAGATTCTTCTCAAATCTGATGGACTCAGGACGTT GCAATCTGTGTGGGGAAGAGAGC 28 CODING GCTACTCTAGCTCGCATTGACCTGGAGCGCAGAATTGAATCTCTCA ACGAGGAGATCGCGTTCCTTAAGAAAGTGCA 29 CODING AGGTGACGGTGCTGAAGAAGGCCCTGGATGAAGAGACGCGGTCCC ATGAGGCTCAGGTCCAGGAGATGAGGCAGAAACACGCACAGGCGG T 30 CODING CCCAGAGCGGAAGTACTCAGTCTGGATCGGGGGCTCTATCCTGGCC TCTCTCTCCACCTTCCAGCAGATGTGGATCAGCAAGCCTGAGTATG ATGAGGCAGGGCCCTCCATTGTCCACAGGAAGTGCT 31 CODING TTGCCAGCACCGTGGAAGCTCTGGAAGAGGGGAAGAAGAGGTTCC AGAAGGAGATCGAGAACCTCACCCAGCAGTACGAGGAGAAGGCGG CCGCTTATGATAAACTGGAAAAGACCAAGAACAGGCTTCAGCAGG AGCTGGACGACCTGGTTGTTGATTTGGACAACCAGCGGCAACTCGT G 32 CODING GCCATCCCGCTTAGCCTGCCTCACCCACACCCGTGTGGTACCTTCA GCCCTGGC 33 CODING GAAAAGGCCAAGAATCTTACCAAGCTGAAAA 34 CODING GCAGCTGACCGCCATGAAGGTGATTCAGAGGAACTGCGCCGCCTA CCT 35 CODING CGCAGAAGGGCCAACTCAGTGACGATGAGAAGTTCCTCTTTGTGGA CAAAAACTTCATCAACAGCCCAGTGGCCCAGGCTGACTGGGCCGCC AAGAGACTCGTCTGGGTCCCCTCGGAGAAGCAGGGCTTCGAGGCA GCCAGCATTAAGGAGGAGAAGGGGGATGAGGTGGTTGTGGAGCTG GTGGAGAATGGCAAGAAGGTCACGGTTGGGAAAGATGACATCCAG AAGATGAACCCACCCAAGTTCTCCAAGGTGGAGGACATGGCGGAG CTGACGTGCCTCAACGAAGCCTCCGTGCTACACAACCTGAGGGAGC GGTACTTCTC 36 CODING TGAGAGCGTCACAGGGATGCTTAACGAGGCCGAGGGGAAGGCCAT TAAGCTGGCCAAGGACGTGGCGTCCCTCAGTTC 37 CODING AAAACGGGCAATGCTGTGAGAGCCATTGGAAGACTGTCCTC 38 CODING CTACGAGATCCTGGCGGCGAATGCCATCCCCAA 39 CODING CTGCAACTTGAGAAGGTCACGGCTGAGGCCAAGATCAAG 40 CODING AGAACCCCACAGACGAATACCTGGAGGGCATGATGAGCGAGGCCC CGGGGCCCATCAACTTCACCATGTTCCTCACCATGTTTGGGGAGAA GCTGAACGGCACGGACCCCGAGGATGTGATTCGCAACGCCTTTGCC TGCTTCGACGAGGAAGCCTCA 41 CODING CCACATCTCTTTCTTATTGGCTGCATTGGAGTTAGTGGCAAGACGA AGTGGGATGTGCTCGATGGGGTGGTTAGACGGCTGTTCAAA 42 CODING GGTCAAGGAACTCAAGGTTTCGCTGCCGTGGAGTGGATGCCAATAG AAACTGG 43 CODING TTACCGGCGGGGAGCTGTTTGAAGACAT 44 CODING GCAGATGATGGCGGCTTGACTGAACAGAGTG 45 CODING TAGGGCCTGAGCTGCCTATGAATTGGTGGATTGTTAAGGAGAGGGT GGAAATGCATGACCGATGTGCTGGGAGGTCTGTGGAAATGTGTGA CAAGAGTGTGAGTGTGGAAGTCAGCGTCTGCGAAACAGGCAGCAA CACAGAGGAGTCTGTGAACGACCTCACACTCCTCAAGACAAACTTG AATCTCAAAGAAGTGCGGTCTATCGGTTGTGGAGATTGTTCTGTTG ACGTGACCGTCTGCTCTCCAAAGGAGTGCGCCTCCCGGGGCGTGAA CACTGAGGCTGTTAGCCAGGTGGAAGCTGCCGTCATGGCAGTGCCT CGTACTGCAGACCAGGACACTAGCACAGATTTGGAACAGGTGCAC CAGTTCACCAACACCGAGACGGCCACCCTCATAGAGTCCTGCACCA ACACTTGTCTAAGCACTTTGGACAAGCAGACCAGCACCCAGACTGT GGAGACGCGGACAGTAGCTGTAGGAGAAGGCCGTGTCAAGGACAT CAACTCCTCCACCAAGACGCGGTCCATTGGTGTTGGAACGTTGCTT TCTGGCCATTCTGGGTTTGACAGGCCATCAGCTGTGAAGACCAAAG AGTCAGGTGTGGGGCAGATAAATATTAACGACAACTATCTGGTTGG TCTCAAAATGAGGACTATAGCTTGTGGGCCACCACAGTTGACTGTG GGGCTGACAGCCAGCAGAAGGAGCGTGGGGGTTGGGGATGACCCT GTAGGGGAATCTCTGGAGAACCCCCAGCCTCAAGCTCCACTTGGAA TGATGACTGGCCTGGATCACTACATTGAGCGTATCCAGAAGCTGCT GGCAGAACAGCAGACACTGCTGGCTGAGAACTACAGTGAACTGGC AGAAGCTTTCGGGGAACCTCA 46 CODING ATTGGCCTGGACCAGATCTGGGACGACCTCAGAGCCGGCATCCAGC AGGTGTACACACGGCAGAGCATGGCCAAGTCCA 47 CODING CAGTAGAGCCAAGTTGGGAGGTGGTGAAAA 48 CODING CTGTGTCCAGTCAGGCTGCGCAGGCG 49 CODING GTTGGTGGTTCGTCAGCACTGCCGAGGAGCAAGGCTGGGTCCCTGC AACGTGCCTCGAAGGC 50 CODING GGGGCAGACACTACCGAAGATGGGGATGAGAAGAGCCTGGAGAA ACAGAAGCACAGTGCCACCACTGTGTTCGGAGCAAACACCCCCA 51 CODING TATGCGCTGATGGAGAAAGACGCCCTCCAGGTGGCC 52 CODING GGTTAGAGTGGACAGCCCCACTATG 53 CODING TCCTGGGGGACCAGACGGTCTCAGACAATGAG 54 CODING GGTGCAGACCGTACTCCATCCCTCCCTGTGAGCACCACGTCAACGG CTCCCGGCC 55 CODING CAGAGTCCGCCCAGTCATGCACAGACTCCAGTGGAAGTTTTGCCAA ACTGAATGGTCTCTTTGACAGCCCTGTCAAGGAATACCAACAGAAT ATTGATTCTCCTAAACTGTATAGTAACCTGCTAACCAGTCGGAAAG AGCTACCACCCAATGGAGATACTAAATCCATGGTAATGGACCATCG AGGGCAACCTCCAGAGTTGGCTGCTCTTCCTACTCCTGAGTCTACA CCCGTGCTTCACCAGAAGACCCTGCAGGCCATGAAGAGCCACTCAG AAAAGGCCCATGGCCATGGAGCTTCAAGGAAAGAAACCCCTCAGT TTTTTCCGTCTAGTCCGCCACCTCATTCCCCATTAAGTCATGGGCAT ATCCCCAGTGCCATTGTTCTTCCAAATGCTACCCATGACTACAACA CGTCTTTCTCAAACTCCAATGCTCACAAAGCTGAAAAGAAGCTTCA AAACATTGATCACCCTCTCACAAAGTCATCCAGTAAGAGAGATCAC CGGCGTTCTGTTGATTCCAGAAATACCCTCAATGATCTCCTGAAGC ATCTGAATGACCCAAATAGTAACCCCAAAGCCATCATGGGAGACA TCCAGATGGCACACCAGAACTTAATGCTGGATCCCATGGGATCGAT GTCTGAGGTCCCACCTAAAGTCCCTAACCGGGAGGCATCGCTATAC TCCCCTCCTTCAACTCTCCCCAGAAATAGCCCAACCAAGCGAGTGG ATGTCCCCACCACTCCTGGAGTCCCAATGACTTCTCTGGAAAGACA AAGAGGTTATCACAAAAATTCCTCCCAGAGGCACTCTATATCTGCT ATGCCTAAAAACTTAAACTCACCAAATGGTGTTTTGTTATCCAGAC AGCCTAGTATGAACCGTG 56 CODING TTAGCCATCCTGGTGATAGTGATTATGGAGGTGTACAAATCGTGGG CCAAGATGAGACTGATGACCGGCCTGAATGTCCCTATGGACCATCC TGTTA 57 CODING CCTCCTTCTCAGTAGCAGAGTCCAGTGCCTTGCAGAGCCTGAAGCC TGGGGA 58 CODING GTTGCCAGAGGTGTACTGTGTCATCAGCCGCCTTGGCTG 59 CODING GTGCATCAAGTACATGCGGCAGATCTCGGAGGGAGTGGAGTACAT CCACAAGCAGGGCATCGTGCACCTGGACCTCAAGCCGGAGAACAT CATGTGTGTCAACAAGACGGGCACCAGGATCAAGCTCATCGACTTT GGTCTGGCCAG 60 CODING TTGGGTCAGTTCCAACATGCCCTGGATGAGCTCCTGGCATGGCTGA CACACACCGAGGGCTTGCTAAGTGAGCAGAAACCTGTTGGAGGAG ACCCTAAAGCCATTGAAA 61 CODING TTTGAAGATTCTGCAACCGGGGCACAGCCACCTTTATAACAACC 62 CODING TGCTTGCCATATCCAATTGAACACCCCTACCACACACACATCTGTC GCGGCGCC 63 CODING TCTGGAGTCAATACCTGGCGAGATCAACTGAGACCAACACAGCTGC TTCAAAATGTCGCCAGATTCAAAGGCTTCCCACAACCCATCCTTTC CGAAGATGGGAGTAGAATCAGATATGGAGGACGAGACTACAGCTT G 64 CODING AAAGCTGGACAAGATCTGGCCTAAGCTTCGGGTCCTGGCGCGATCT TCTCCCACTGACAAG 65 CODING GTAGGAGAGTTGAGTGCTGCAATGGAT 66 CODING GTTCACCAACCCATGCAAGACCATGAAGTTCATCGTGTGGCGCCGC TTTAAGTGGGTCATCATCGGCTTGCTGTTCCTGCT 67 CODING TTCGGATCTACCCTCTGCCGGATGACCCCAGCGTGCCAGCCCCTCC CAGACAGTTTCGGGAATTACCTGACAGCGTCCCACAGGAATGCACG GTTAGGATTTACATTGTTCGAGGCTTAGAGCTCC 68 CODING TCTGGTCTTTGAGAAGTGCGAGCTGGCGACCTGCACTCCCCGGGAA CCTGGAGTGGCTGGCGGAGACGTCTGCTCCTCCGACTCCTTCAACG AGGACATCGCGGTCTTCGCCAAGCAG 69 CODING GTACAGGACAGCCAGCGTCATCATTGCTTTGACTGATGGAG 70 CODING CTGAGGTCACCCAGTCAGAGATTGCTCAGAAGCAAA 71 CODING TTTCCACCGCAAAGCATCAGTGATCATGGTAGACGAGCTGCTGTCA GCCTACCCACACCAGCTTTCCTTCTCTGAGGCTGGCCTTCGAATCAT GATAACCAGCCACTTTCCCCCCAAGACCCGGCTCTCCATGGCCAGT CGCATGTTGATCAATGA 72 CODING CGGCAGCGGTGGAAGGCCCTTTTGTCACCTTGGACATGGAAG 73 CODING CGGCGGCCCATGGACTCAAGGCTGGAGCACGTGGACTTTGAGTGCC TTTTTACCTGCCTCAGTGTGCGCCAGCTCATCCGAATCTTTGCCTCA CTG 74 CODING TACGATGAGCTGCCCCATTACGGCGGG 75 CODING TGCGGGACCACAATAGCGAGCTCCGCTTC 76 CODING CTGCTCGTTGCTCTGTCTCAGTATTTCCGCGCACCAATTCGACTCCC AGACCATGTTTCCATCCAAGTGGTTGTGGTCCAG 77 CODING GGCTGTGGTGTCTCTTCATTGGGATTGGAGA 78 CODING TGCAGGGAGTTCCAGCGAGGAAACTGTGCCCGGGGAGAGACCGAC TGCCGCTTTGCACACCCCGCAGACAGCACCATGATCGACACAAGTG ACAACACCGTAACCGTTTGTATGGATTACATAAAGGGGCGTTGCA 79 CODING GAGCCCAGTGAAGGCCTCATATTCCCCTGGGTTCTGAATATAACTA GAGCCCCTTAGCCCCAACGGCTTTCCTAAATTTTCCACATCCAAGC CTAACAGTCTCCCCATGTGTTTGTGTA 80 CODING GCCTTTGACACCTTGTTCGACCATGCCCCAGACAAGCTGAATGTGG TGA 81 CODING GGAGAAGAACCTGCTACAGGAACAGCTGCAGGCAGAGACAGAGCT GTATGCAGAGGCTGAGGAGATGCGGGTGCGGCTGGCGGCCAAGAA GCAGGAGCTGGAGGAGATACTGCATGAGATGGAGGCCCGCCTGGA GGAGGAGGAAGACAGGGGCCAGCAGCTACAGGCTGAAAGGAAG 82 CODING CTCCTTGAGGAGAGGATTAGTGACTTAACGACAAATCTTGCAGAAG 83 CODING AAGGGGTTCTGAGGTCCATACCAAGAAGACGGTGATGATCAAGAC CATCGAGACACGGGATGG 84 CODING GAAGAAGATCAATGAGTCAACCCAAAATT 85 CODING GCCAAGGCGAACCTAGACAAGAATAAGCAGACGCTGGAGAAAGA GAACGCAGACCTGGCCGGGGAGCTGCGGGTCCTGGGCCAGGCCAA GCAGGAGGTGGAACATAAGAAGAAGAAGCTGGAGGCGCAGGTGC AGGAGCTGCAGTCCAAGTGCAGCGATGGGGAGCGGGCCCGGGCGG AGCTCAATGACAAAGT 86 CODING TCTCTTCCAAATACGCGGATGAGAGGGACAGAGCTGAGGCAGAAG CCAGGGAGAAGGAAACCAAGGCCCTGTCCCTGGCTCGGGCCCTTG AAGAGGCCTTGGAAGCCAAAGAGGAACTCGAGCGGACCAACAAAA TGCTCAAAGCCGAAATGGAAGACCTGGTCAGCTCCAAGGATGACG TGGGCA 87 CODING GCCTCTTCTGCGTGGTGGTCAACCCCTATAAACACCTGCCCATCTAC TCGGAGAAGATCGTCGACATGTACAAGGGCAAGAAGAGGCACGAG ATGCCGCCTCACATCTACGCCATCGCAGACACGGCCTACCGGAGCA TGCTTCAA 88 CODING TGAAGCCCCACGACATTTTTGAGGCCAACGACCTGTTTGAGAACAC CAACCATACACAGGTGCAGTC 89 CODING CTTGAGTCCCTGAGAATGCCTAGCAAAGTCCTCAACTTACTTAATTT CAGATATGTCACCTCCTAATCTGGGTCCAAGGAGTATAATATTTTT AATGAGTCAAAAATCCAACTCAGATTGACCTAAAATATATTTATCT TCTTTGCACACTTAAAAAATCCAGGAGCACCCCAAAATAGACATGT ACCGTTATATTAAGTAAGCAGGAGACTTAGGATTTGTGCTGTAGCC ACAAGAAAGACAGTGATCAGTGATATCAAACATCAGGAATCAGCC TTTATGTAACATAACAGCTGTCCTCCTATGGTGAAAGGTTCAAATG TAGTGAAGGTATAACCTATATTGACTGAGATTTCCCTTTTAGGTAGT GCCTTATCTCTATTACTAGTGTTAAAGGAATAAGGAATCTATGAAG GACAGGGAGCAGCTCTGGTCTGTCAATCTCAGCCACCTGTTTGATA TCACAGAGAAGATACTCGGAGGATTGTTGGAATGTATATAGTTTAG TAAGAAGTGGGTAAGAAAGAGGGTCTTAATTACTGAGCACTTATTA TGTATTAGGTTCTTTGCCAGATGTTTTTACATATATAAACTCATTTC AGAAAACTTATTTAAAGTAAATGGGGCCGGGTATGGTGGTTCATGC CTGGAATCCTAGCACTTTGGGAGGCTGAGGTAGGAGGACTGCTTGA GGCCGGGAGTTGGAGACCAGCCTGAGCAACATAGTGAGACCCTGT CTCAATAATAATAATAATAATAGTAATAATGAAGTAAATGGGATA AGGAAAGAAGGATAATTATCTTTAAAGGTTGATTCCCACCCTCCCT CCCCAGTTACTTAAGGAACTAAGTGAGTACATCTCCAGTTGCCCAT GAAAGCATAAGTTTGTTTTCCTCAGCTGAGGCAAGTGGTAGAGTAT ACAGGATAACGAAGTAACATGTAAAAGGCAGGACGCACATAAAGG TGTACATGGCTATTGTTTCACCTGGAGAAACCACATGATTGGGACC TGAAGGTTTACTGACTGACTACAGGGGCTGATTGTGAAGCACGAGG AACCCCATGTGTGTGGAGACTGTAGGGTGAGAGCACACAATTATTA GCATCATTTCTGAGTGATCTCACAGATTTTTTTTCTTGTGTTTGCTTT GCTTTTTGACAACTGCTTCTCCCACGTTCCTTGCAATTCTATTCTCTC ACCTTCACTTTACTATTTGTATTCGATGGACCAGGATAATTCAGGCA AGGTTACCTTGTAAACTTTAATTGGCCACACACCATGTTGTCACCC AGCTGGCTATGAAGTGAATAATGGTACTGAAAGTAAACCTGAAGA CCTTTCTCAGATCTATTTTAAGTCTGAGTCTGACCAACCATGGAAA ATATTCGACATGAATTAATGTAGAGAACTATAAAGCATTTATGACA GCTCCAAGAAAAATCATCTACTCTATGCAGGAGATATGTTTAGAGA CCTCTCAGAAAAACTTGCCTGGTTTGAGGGTACACA 90 CODING ACGGACAAGTCTTTCGTGGAGAAGCTGTGCACGGAGCAGGGCAGC CACCCCAAGTTCCAGAAGCCCAAGCAGCTCAAGGACAAGACTGAG TTCTCCATCATCCATTATGC 91 CODING GAGAATGAGCTTAAGGAGCTGGAACAG 92 CODING GGGGCAACCAATGGAAAAGACAAGACA 93 CODING TGCTTCAAGAAGAAACCCGGCAGAAGCTCAACGTGTCTACGAAGC TGCGCCAG 94 CODING ACAAATCCTATCACTATACCGACTCACTACTACAGAGGGAAAATGA AAGGAATCTATTTTCAAGGCAGAAAGCACCTTTGGCAAGTTTCAAT CACAGCTCGGCACTGTATTC 95 CODING AGCAAAATCTTCTTCCGAACTGGCGTCCTGGCCCACCTAGAGGAGG AGCGAGATTTGAAGATCACCGATGTCATCATGGCCTTCCAGGCGAT GTGTCGTGGCTACT 96 CODING GTGTGGAAACCATCTGTTGTGGAAGAGTAA 97 CODING TCTACAGTTTTGCACCACGGCAAGAAAACCAAAAACCAAAACAAA CAAACAAAAAAAACCCAACAACAACCCAGAACAAAGCAAAACCC AGCAGACTGTACTTAGCATTGTCTAAATCCATTCTCAAATTCCAAA TATCACAGACACCCCTCACACAAGGAATATAAAAACCACCACCCTC CAGCCTGGGCAACGTAGTAAAACCTCATCTATACAAGAATTTAAAA ATAAGCTGGGCGTGGTGGTACACACCTGTGGTCCCAGCTACTAGGG AGGCTGAGCCAGGAAGAACGCTCCAGCCCAGGACTTCGAGGCTGC AATGAGCTATAATTGCATCATTGCACTCCAGCCTGGGCAACAGAGA CCCTGTCTCAACCACCACCACCACCACCACCCCTACTACCCCTGTAT TCAAGGTAAAAATTGAAGTTTGTATGATGTAAGAGATGAGAAAAA CCCAACAGGAAACACAGACACATCCTCCAGTTCTATCAATGGATTG TGCAGACACTGAGTTTTTAGAAAAACATATCCACGGTAACCGGTCC CTGGCAATTCTGTTTACATGAAATGGGGAGAAAGTCACCGAAATGG GTGCCGCCGGCCCCCACTCCCAATTCATTCCCTAACCTGCAAACCTT TCCAACTTCTCACGTCAGGCCTTTGAGAATTCTTTCCCCCTCTCCTG GTTTCCACACCTCAGACACGCACAGTTCACCAAGTGCCTTCTGTAG TCACATGAATTGAAAAGGAGACGCTGCTCCCACGGAGGGGAGCAG GAATGCTGCACTGTTTACACCCTGACTG 98 NON_CODING CAGCAGTTGATACCTAGCAGCGTTATTGATGGGCATTAATCTATGT (UTR) TAGTTGGCACCTTAAGATACTAGTGCAGCTAGATTTCATTTAGGGA AATCACCAGTAACTTGACTGACCAATTGATTTTAGAGAGAAAGTAA CCAAACCAAATATTTATCTGGGCAAAGTCATAAATTCTCCACTTGA ATGCGCTCATGAAAAATAAGGCCAAAACAAGAGTTCTGGGCCACA GCTCAGCCCAGAGGGTTCCTGGGGATGGGAGGCCTCTCTCTCCCCA CCCCCTGACTCTAGAGAACTGGGTTTTCTCCCAGTACTCCAGCAATT CATTTCTGAAAGCAGTTGAGCCACTTTATTCCAAAGTACACTGCAG ATGTTCAAACTCTCCATTTCTCTTTCCCCTTCCACCTGCCAGTTTTGC TGACTCTCAACTTGTCATGAGTGTAAGCATTAAGGACATTATGCTT CTTCGATTCTGAAGACAGGTCCCTGCTCATGGATGACTCTGGCTTCC TTAGGAAAATATTTTTCTTCCAAAATCAGTAGGAAATCTAAACTTA TCCCCTCTTTGCAGATGTCTAGCAGCTTCAGACATTTGGTTAAGAAC CCATGGGAAAAAAAAAATCCTTGCTAATGTGGTTTCCTTTGTAAAC CAGGATTCTTATTTGTGCTGTTATAGAATATCAGCTCTGAACGTGTG GTAAAGATTTTTGTGTTTGAATATAGGAGAAATCAGTTTGCTGAAA AGTTAGTCTTAATTATCTATTGGCCACGATGAAACAGATTTC 99 NON_CODING GGCCGAGGGAGTCTATGAAAATCTCCCCTTTTTTACTTTTTTAAAGA (UTR) GTACTCCCGGCATGGTCAATTTCCTTTATAGTTAATCCGTAAAGGTT TCCAGTTAATTCATGCCTTAAAAGGCACTGCAATTTTATTTTTGAGT TGGGACTTTTACAAAACACTTTTTTCCCTGGAGTCTTCTCTCCACTT CTGGAGATGAATTTCTATGTTTTGCACCTGGTCACAGACATGGCTT GCATCTGTTTGAAACTACAATTAATTATAGATGTCAAAACATTAAC CAGATTAAAGTAATATATTTAAGAGTAAATTTTGCTTGCATGTGCT AATATGAAATAACAGACTAACATTTTAGGGGAAAAATAAATACAA TTTAGACTCTAAAAAGTCTTTTCAAAAAGAAATGGGAAATAGGCAG ACTGTTTATGTTAAAAAAATTCTTGCTAAATGATTTCATCTTTAGGA AAAAATTACTTGCCATATAGAGCTAAATTCATCTTAAGACTTGAAT GAATTGCTTTCTATGTACAGAACTTTAAACAATATAGTATTTATGGC GAGGACAGCTGTAGTCTGTTGTGATATTTCACATTCTATTTGCACAG GTTCCCTGGCACTGGTAGGGTAGATGATTATTGGGAATCGCTTACA GTACCATTTCATTTTTTGGCACTAGGTCATTAAGTAGCACACAGTCT GAATGCCCTTTTCTGGAGTGGCCAGTTCCTATCAGACTGTGCAGAC TTGCGCTTCTCTGCACCTTATCCCTTAGCACCCAAACATTTAATTTC ACTGGTGGGAGGTAGACCTTGAAGACAATGAAGAGAATGCCGATA CTCAGACTGCAGCTGGACCGGCAAGCTGGCTGTGTACAGGAAAATT GGAAGCACACAGTGGACTGTGCCTCTTAAAGATGCCTTTCCCAACC CTCCATTCATGGGATGCAGGTCTTTCTGAGCTCAAGGGTGAAAGAT GAATACAATAACAACCATGAACCCACCTCACGGAAGCTTTTTTTGC ACTTTGAACAGAAGTCATTGCAGTTGGGGTGTTTTGTCCAGGGAAA CAGTTTATTAAATAGAAGGATGTTTTGGGGAAGGAACTGGATATCT CTCCTGCAGCCCAGCACCGAGATACCCAGGACGGGCCTGGGGGGC GAGAAAGGCCCCCATGCTCATGGGCCGCGGAGTGTGGACCTGTAG ATAGGCACCACCGAGTTTAAGATACTGGGATGAGCATGCTTCATTG GATTCATTTTATTTTACACGTCAGTATTGTTTTAAAGTTTCTGTCTGT AAAGTGTAGCATCATATATAAAAAGAGTTTCGCTAGCAGCGCATTT TTTTTAGTTCAGGCTAGCTTCTTTCACATAATGCTGTCTCAGCTGTA TTTCCAGTAACACAGCATCATCGCACTGACTGTGGCGCACTGGGGA ATAACAGTCTGAGCTAGCACCACCCTCAGCCAGGCTACAACGACA GCACTGGAGGGTCTTCCCTCTCAGATTCACCTGGAGGCCCTCAGAC CCCCAGGGTGCACGTCTCCCCAGGTCCTGGGAGTGGCTACCGCAGG TAGTTTCTGGAGAGCACGTTTTCTTCATTGATAAGTGGAGGAGAAA TGCAGCACAGCTTTCAAGATACTATTTTAAAAACACCATGAATCAG ATAGGGAAAGAAAGTTGATTGGAATAGCAAGTTTAAACCTTTGTTG TCCATCTGCCAAATGAACTAGTGATTGTCAGACTGGTATGGAGGTG ACTGCTTTGTAAGGTTTTGTCGTTTCTAATACAGACAGAGATGTGCT GATTTTGTTTTAGCTGTAACAGGTAATGGTTTTTGGATAGATGATTG ACTGGTGAGAATTTGGTCAAGGTGACAGCCTCCTGTCTGATGACAG GACAGACTGGTGGTGAGGAGTCTAAGTGGGCTCAGTTTGATGTCAG TGTCTGGGCTCATGACTTGTAAATGGAAGCTGATGTGAACAGGTAA TTAATATTATGACCCACTTCTATTTACTTTGGGAAATATCTTGGATC TTAATTATCATCTGCAAGTTTCAAGAAGTATTCTGCCAAAAGTATTT ACAAGTATGGACTCATGAGCTATTGTTGGTTGCTAAATGTGAATCA CGCGGGAGTGAGTGTGCCCTTCACACTGTGACATTGTGACATTGTG ACAAGCTCCATGTCCTTTAAAATCAGTCACTCTGCACACAAGAGAA ATCAACTTCGTGGTTGGATGGGGCCGGAACACAACCAGTCTT 100 NON_CODING CAGCTTGCAGCCCAACCGAGATACAAACAGAACATCATTGCAAGA (INTRONIC) ACTCAGGCCCCATCTGACTACCCCTCCCCTGAAGACTCAAAGAGGG ACCGTCTTTTTGGCGAGCAGGCCTGTTGAGTGTGGGTGATTTCTTGG CTCAGCTAGAAGCATCCCTCCAGAAGGGGGCCCGTTTTGTGAAATG AGAATAAGCCCTTTCCTTCCATAGCGAGATCTTCCTCCACGTCGGG 101 NON_CODING CTGCCACCAGAGACCGTCCTCACCCC (UTR) 102 NON_CODING CCTCTACAGGGTTAGAGTTTGGAGAGAGCAGACTGGCGGGGGGCC (UTR) CATTGGGGGGAAGGGGACCCTCCGCTCTGTAGTGCTACAGGGTCCA ACATAGAGCCGGGTGTCCCCAACAGCGCCCAAAGGACGCACTGAG CAACGCTA 103 NON_CODING CAAGGATCCCCTCGAGACTACTCTGTTACCAGTCATGAAACATTAA (UTR) 104 NON_CODING CCCAGATGTCATTCGTGCTGAAAGAACCAGAACAACTCTCTGCTCC (UTR) CTGCCAAGCATGAAGCGGTTGTGACCCCAGGAAACCACAGTGACTT TGACTCTGGTTCAGCTGACATGCTCGAGTC 105 NON_CODING CAGTGGCGTTTGTAATGAGAGCACTTTCTTTTTTTTCTATTTCACTG (UTR) GAGCACAATAAATGGCTG 106 NON_CODING GGAGCAAACTGCATGCCCAGAGACCCAGCGGACACACGCGGTTTG (UTR) GTTTGCAGCGACTGGCATACTATGTGGATGTGA 107 NON_CODING TGGTCCCCAACAGCGACATAGCCCATCCCTGCCTGGTCACAGGGCA (UTR) TGCCCCGGCCACCT 108 NON_CODING CAAGCAACAGAGGACCAATGCAACAAGAACACAAATGTGAAATCA (UTR) TGGGCTGACTGAGACAATTCTGTCCATGTA 109 NON_CODING TGCAGCCATGGTCACGAGTCATTTCTGCCTGACTGCTCCAGCTAAC (UTR) TTCCAGGGTCTCAGCAAACTGCTGTTTTTCACGAGTATCAACTTTCA TACTGACGCGTCTGTAATCTGTTCTTATGCTCATTTTGTATTTTCCTT TCAACTCCAGGAATATCCTTGAGCATATGAGAGTCACATCCAGGTG ATGTGCTCTGGTATGGAATTTGAAACCCCAATGGGGCCTTGGCACT AAGACTGGAATGTA 110 NON_CODING GGCTCTGTCACTGAGCAATGGTAACTGCACCTGGGCA (UTR) 111 NON_CODING GCTGCTGTCACAAATACCCATCTTAGGATCCCATCAGCTTCCCATCC (UTR) CCCACCAGACAGCCACAGTACCCTCACTTTCTCCCTATTGTTCTTTC AAATCCTGTTCTCAGGAAAGAAACTGCCACTAATTCATTCACACTA AGGTGTAAATGATTGATAATAGGAATGAGTTACCTCTTCCCACAGA CATTTGTTTTTAAGTATGACAGAGCAGGGCCTTAATCCCAAGGGAA AAGGTTATGGAACTGGAGGGGGTGAGCTTTCTGGGTAGAAGGAGA CTTCCTGAATTTCCTTAAAACCCAGTAAGAGTAAGACCTGTTGTTTT GGAAGGTCTGCTCCACCATCTAAGAGCACTGTTTTTTTTTTTTTGTT GTTGTTGTTGTTTTACGGTCTCTGAGGGAATATAGTAAAAATGCAT ATGCACGTGCAATTTGCACGGCAGCATTTCACCGATTGTGGACTGT ATTGGCTAATGTGTTTCCTGGTCTTTAGATGCAAACCATTAATAACA CTATCTTATCTCATAGTTTTTTCAGGGGTGCTTCTTGATTAGTAGGG AATTTTGAACACCTCTTTAAATACAGCTAGAAAATAAAACCAATTT GTAAAGCCACATTTGCATATGATGCCAGCCTCACGCATTTGTATAT CTCCAGAAATTCAGGTATGCCTCACCAATTTGCCCGTC 112 NON_CODING TCTTCTGTTGCAGGACTAACCTTTGAGAAATCCTTTTGTGAAGTCAT (UTR) TGCCTGCTCAAGAATGTACAGTGGCTCCCCAATGCCTTGGAGGCCA TAAGGCCAGCCAGTTCTAGCTCTCTATTACCTGTCCCCACTCAACTG ACTCATACCTGTTTCCGGCTGCATCACTATGTGCCCCACAGAGAAC GATGATCGTCACCTCTGTGCCTGA 113 NON_CODING ATCATTGAATGGATCGGCTATGCCCTGGCCACTTGGTCCCTCCCAG (ncTRANSCRIPT) CACTTGCATTTGCATTTTTCTCACTTTGTTTCCTTGGGCTGCGAGCTT TTCACCACCATAG 114 NON_CODING TCCAGTGTTCGCCATTCCAGATGTCACTTTGCGTCCTCAGAGGGGA (INTRONIC) CTCTGGGGCAGCCACCATGGCCGGCTTGTCTGGAGGCCCTTGGAGA TCTAGGATGGGCGCTGGTCGTGGCTTTGGAGAACTTTCCTTCTCCA AACAAATGCAGGAAACTCAAGATTCAGCATCCTAGAATTGTCTCTG GCAAGTTGGTTTCCAGCCATAGTGAGTGGGAACAATGGCCCCAGA GGCTGTGTGGCAGTTTAAACACAGTTTCCACTGCCTTCCCTTTCCCT AAAGAGTAAACACAGGAGATAATACTTTCTAACAACTCATCGTTAT CAAGGGCCTACTATGTGCTGCTTGTTTTGGCTGCATGCGTAAACAC ATCTC 115 NON_CODING GTCAGATCCGAGCTCGCCATCCAGTTTCCTCTCCACTAGTCCCCCCA (UTR) GTTGGAGATCT 116 NON_CODING TATAACCTTTGTGTGCGTGTATGTTGTGTGTGTGCATGTGTGGCGTA (UTR) TATGTGTGTTACAGGTTAATGCCTTCTTGGAATTGTGTTAATGTTCT CTTGGTTTATTATGCCATCA 117 NON_CODING TCCAAATCATTCCTAGCCAAAGCTCTGACTCGTTACCTATGTGTTTT (UTR) 118 NON_CODING TGTGATTCTAAGTCAGGCCCTTGTGACTGAACCACCATGAGGCTGG (INTRONIC) ACTGTGGGGACTCGGGTATCCCAGAGGCAGAGCACACCAGGTCTG GGAGGGGGGCCACTCAGACGGCAACATTGTC 119 NON_CODING GATCACGCCGTTATGTTGCCTCAAATAGTTTTAGAAGAGAAAAAAA (UTR) AATATATCCTTGTTTTCCACACTATGTGTGTTGTTCCCAAAAGAATG ACTGTTTTGGTTCATCAGTGAATTCACCATCCAGGAGAGACTGTGG TATATATTTTAAACCTGTTGGGCCAATGAGAAAAGAACCACACTGG AGATCATGATGAACTTTTGGCTGAACCTCATCACTCGAACTCCAGC TTCAAGAATGTGTTTTCATGCCCGGCCTTTGTTCCTCCATAAATGTG TCCTTTAGTTTCAAACAGATCTTTATAGTTCGTGCTTCATAAGCCAA TTCTTATTATTATTTTTGGGGGACTCTTCTTCAAAGAGCTTGCCAAT GAAGATTTAAAGACAGAGCAGGAGCTTCTTCCAGGAGTTCTGAGCC TTGGTTGTGGACAAAACAATCTTAAGTTGGGCAGCTTTCCTCAACA CAAAAAAAAGTTATTAATGGTCATTGAACCATAACTAGGACTTTAT CAGAAACTCAAAGCTTGGGGGATAAAAAGGAGCAAGAGAATACTG TAACAAACTTCGTACAGAGTTCGGTCTATTAATTGTTTCATGTTAGA TATTCTATGTGTTTACCTCAATTGAAAAAAAAAAGAATGTTTTTGCT AGTATCAGATCTGCTGTGGAATTGGTATTGTATGTCCATGAATTCTT CTTTTCTCAGCACGTGTTCCTCACTAGAAGAA 120 NON_CODING TTGGGTTGTCACTCTAGAGCATGTCAAACTTTGTACTTCAAAATATA (INTRONIC) TTTAGTATGATTGTTAGTGGTAACATATATCAAGGCTTTGAATTAAC TGTTTTATTTAATTTTCACAAGAAGCACTTATTTTAGCCATAGGAAA ACCAATCTGAGCTACAAATAGTTCTTTAAAATAAGCCCAGGTTATT TAGCTATTCTAGAAAGTGCCGACTTCTTTCAAGAAGCAGGCATTGT AGGACAGCTGAGAATTATCACATAGCCTAAATTCTAGCCTGGCAGC AAGAGTCACATCTGAGATGTCCAAAAAAAAAAAAAAAACACCTGA TCTACATTGAAAGGGGGTAGACTAACGTATGTGAGACCATTTTCCT ATTTGCAGTTACAAGGTTAAAGAACTTTGAAGGTCATTCGGCTGCT AAGAGGCATGTCGAACACTCTGTGTGGCTCTTTCACAGTAAACCCT CCTAAGAGCAGAAGACACATGGCTGTTAGTGTCTGCGTTTAGATTT AATTTCTCAAATAAAGGCCCTTGGCTGCGTATCATTTCATCCAGTTA TAAACTAGGGCTCCTGCAAGCACCCCCATTCTAAGGGTGAATTATT GAAATCAGTTGCTATTTGATGAGTCACAACTGGCCCAGCAGGCAGG GCATTTGAAGTCATGGTCATCAAAAAGAAATGATTGTTTTTTGAAA AGCTAAATGCTTAAAATGCTTCTAGAGGGAAGTCGTGGGGCGTGTG CTCATTCTCTTTAAAATCAGGGTTGTTGAGTTTGTTTTTAAACATTT TTATAAGTTCATGAGAAAAAATATATAAATTCTAAGAACCAACACT GTATTCCCAGAAACATGACCCTCGCTGGTCTTGGGTCCACATATCA TTGGACTCTGGGGGACACAAAGATGCCTGTGACACTTTGGTGTTGC CGAGTTAGTCA 121 NON_CODING TCTCTGGGTATAACAAGTCACAAGCAATTCACTCTCCAGTATTAAC (INTRONIC) ACAGAAACTTAATCCAATATTCCTGACAACGAAATCATTTTGCTGC CTATAATGCATCCATGATGATTTACAAAGATAAAGTTTAAATAGTA AAAATTGTATTTTCAGAGTATCCACTACATGCCAAGTTTTTGCACAT GATATGGTAAGGTATGAGATTTCATAGTCACATTACAAAAAAAAAT TTTCCCAGAGAATAAATACAACATTATGGGTATGAGAAGAGGCAA GTAAGTCAAGTCTGCAGGGAGTTTTGAAAAAGAGAAATACTGGAA AGAGCTGCGCTCTCTTGTGTGTTCTCCTGGTGTTCTCCTGTGCTCAC CTCTTAGCTTGCTAAACGTGACCTTCCC 122 NON_CODING CTTGGCACCCACAGTAAGCCTTGTAGGAGCTCAAAGTGCCTCAGGC (INTRONIC) AATCTGTGAGCAGAATAGCAATTTTATTACTTTGTCATTAAACCAA TTTCACAGCAGTATTGTTTGTTAATGAGCAGCGGCAAACGAGCGAA GATGTCACACACTGGAATAGCAGAGAGATTTGTGACCCAAGCTCAC AGCACTAAGATGGAAAGACCACGGCTATAAAAAAGGAAATACTTT GGGATGAAATGCAAAGTCTATACAGCAGAGCTTGTGTTTATGAGCT ACCATTTTGCTAAGAGCTGTGAGAGAAATAAAGGTCTGGAAATATG CAGTTAAAACAGGGCCTATAAAATTAAAACCAAATTAAAGTATAG CAGAGGATTACTGCACAGACTGTACTCGACAAAATATATTTTAAGT GACGAGGTGAAATCTAAATCAGTTTTGTTTGAATTTGGTTGGTATTT ATGAAATTCAATAAAAAAAAATGAAAAAATATCCAAACAAAGCAG CCGCCTCACCCTTGTGTGGTCTCTGAGCCATAAACGTGCATCACTTT GAGGAAATTCAACTTGCCAATCCTTAAATAATTAGCAACTTCTTGA TTCACAGGGTGCGCCCCTCCATCTTCATGAAAGCCTTCTCTGTTACT TTATCTCTTCGTAAGGACGTTGCCCATG 123 NON_CODING GAAAGCCGCACTGCTCTGATGCTGAGATAGTGTTCCTACTTGTTCA (INTRONIC) AGAGTGAGTTCAAAAGTGAGCCTAGCCACCTAATTTTCACTAGCAG CACAGACTGGAAATGCCCAGCAGGATTACAGCTTTGAGACTCACTC TGGAGTACAACAGACTATCCCGCCCCTCTCAGATCAGACCCTAAAG TCTGTTCTAAAATTGTCCACTGTGGGTGCTGAGAGAAGGGGGCCCA AACATAGCGTGTGTTTCATGTCAAACTAATGGGCTACCCTGGAGAG ATTTCAGAGTTCTCATTTGTTTACTCACTTGGGCCCTCAGTCAAGGT CTGATCTTTGGAAGAGCAAATTTTTCCAAATTTTGAATAATCTCTTT CTAGCAAGAGGCTATGAATTCCTTTGTCCATCACTTTTTGGCTACTC GGAGCCACCTTCAACATACCACTCAAAGCTTTTCCTCATTTAACAA TAGGCTGTAATATACTAGTTCTGAACCTTTGCTGGGTCATGGACTTC TC 124 NON_CODING TGCCCACTTGCAAAAGAGGCTGTTGGCAGCAACACTTCACCACTAG (ncTRANSCRIPT) AAACCTTTACTCCAATTCGAAACATGCCTTAACGCACAGTGTGAAT TACCCACTCTCGTGGCCCACAGAGGTTGACTCATTCAGGCCCCCTTT TGTTCAGATGAGGAAACTGAGGCTGACTCCGAAGCCTGGGGGCTTT CAGATGTGGAGTGGGTCCCTGTGCCCAGGTGATGAGGGGACCAGG CGGGTCTGGAGCAGGGCTGGAGTGGGGCTCAGATGTAGTAGGCTG GCAGTTAAAGGTGCCAGATGTGAGCCAGGCTGCTGGGTTTGAATCC TGGAGCTGCCTCATAGCAGCAGTAGGACTTTGGGTAACTTACATAG GTGCTGTATGCCTCAGTGACCTCATCTGTAATATAGAGATGATAAG AGTACCTGTCTCATTGGTCTACTGAGTTGTCCGGATTAACTCATTAA ATGAGTTAAAACTCATGAAGCCCTTGGAACTGTGACTGACACATAG TAAGTACTCAATAAAAAATAACTGCTAAGACCAGCCACAGTGGCTC ACACCTGTAATCTGAGCATTCTGGGAGGCCAAGGCGGAAGAATCC CTTGAGCCCAGTATTTCAAGACCAGCCTAAAGGTCAACATAGGCAG ACTCTGTCTCTACTATACATTTTTAGATTAAATTTTTATAATAATAA TAACCACTAAAATGTGATTACTAAAGACAGCTTCTTCACAGTACAA AGAGATGCTCTTCTGAGTACCAACTCTTTGGAGGATAAACTGCCCT TATACCTTCAAAAATAACACTTGCCATATATCAAGTCCTTTCAAGT ACCTGGAGATTTACCCAGCACTCTGAGATAAATACCATTATCCCTC TGGGCACACAGAGGCTCAGAGAGGTTTAGTCATTTGCCCAAAGTCA CACAGCCTGTACGAGGCCAGGCTGGGACTCAAACTCAGTTCTGACT GATTCTAAAATCATGTGTTTAACTGCTGCACTCTAGGACCACCCGC AATGGATCTGTG 125 NON_CODING CCATCCCGTGTCTCGATGGTCTTGATCATCACCGTCTTCTTGGTATG (INTRONIC) GACCTC 126 NON_CODING CTAGTGCTTGGGATCGTACATGTTAATTTTCTGAAAGATAATTCTAA (UTR) GTGAAATTTAAAATAAATAAATTTTTAATGACCTGGGTCTTAAGGA TTTAGGAAAAATATGCATGCTTTAATTGCATTTCCAAAGTAGCATC TTGCTAGACCTAGTTGAGTCAGGATAACAGAGAGATACCACATGGC AAGAAAAACAAAGTGACAATTGTAGAGTCCTCAATTGTGTTTACAT TAATAGTGGTGTTTTTACCTATGAAATTATTCTGGATCTAATAGGAC ATTTTACAAAATGGCAAGTATGGAAAACCATGGATTCTGAAAGTTA AAAATTTAGTTGTTCTCCCCAATGTGTATTTTAATTTGGATGGCAGT CTCATGCAGATTTTTTAAAAGATTCTTTAATAACATGATTTGTTTGC CTTTCTAGATTTCTTTATCTTTCTGACCAGCAACTTAGGGAGCAGAA TTTAAATTAGGAAGACAAAGGGAAAGATTCATTTAAACCATATTTT TACAAAGTTTGTCATTTGCCCCAAGGTCAAATTTTAAATTCTTAATT TTCATTTTATTTCCCATTTTAGGTAAAAGTTTGCATTTAATCTTAGA ATTATGTTATTTTTGTTAGTAGTGTGGAAACTTAGAGAACTTATTGT ATGGTGCCTTGCA 127 NON_CODING CTCCTATGTCTTTCACCGGGCAATCCAAGTACATGTGGCTTCATACC (ncTRANSCRIPT) CACTCCCTGTCAATGCAGGACAACTCTGTAATCAAGAATTTTTTGA CTTGAAGGCAGTACTTATAGACCTTATTAAAGGTATGCATTTTATA CATGTAACAGAGTAGCAGAAATTTAAACTCTGAAGCCACAAAGAC CCAGAGCAAACCCACTCCCAAATGAAAACCCCAGTCATGGCTTCCT TTTTCTTGGTTAATTAGGAAAGATGAGAAATTATTAGGTAGACCTT GAATACAGGAGCCCTCTCCTCATAGTGCTGAAAAGATACTGATGCA TTGACCTCATTTCAAATTTGTGCAGTGTCTTAGTTGATGAGTGCCTC TGTTTTCCAGAAGATTTCACAATCCCCGGAAAACTGGTATGGCTAT TCTTGAAGGCCAGGTTTTAATAACCACAAACAAAAAGGCATGAAC CTGGGTGGCTTATGAGAGAGTAGAGAACAACATGACCCTGGATGG CTACTAAGAGGATAGAGAACAGTTTTACAATAGACATTGCAAACTC TCATGTTTTTGGAAACTAGTGGCAATATCCAAATAATGAGTAGTGT AAAACAAAGAGAATTAATGATGAGGTTACATGCTGCTTGCCTCCAC CAGATGTCCACAACAATATGAAGTACAGCAGAAGCCCCAAGCAAC TTTCCTTTCCTGGAGCTTCTTCCTTGTAGTTCTCAGGACCTGTTCAA GAAGGTGTCTCCTAGGGGCAGCCTGAATGCCTCCCTCAAAGGACCT GCAGGCAGAGACTGAAAATTGCAGACAGAGGGGCACGTCTGGGCA GAAAACCTGTTTTGTTTGGCTCAGACATATAGTTTTTTTTTTTTTTAC AAAGTTTCAAAAACTTAAAAATCAGGAGATTCCTTCATAAAACTCT AGCATTCTAGTTTCATTTAAAAAGTTGGAGGATCTGAACATACAGA GCCCACATTTCCACACCAGAACTGGAACTACGTAGCTAGTAAGCAT TTGAGTTTGCAAACTCTTGTGAAGGGGTCACCCCAGCATGAGTGCT GAGATATGGACTCTCTAAGGAAGGGGCCGAACGCTTGTAATTGGA ATACATGGAAATATTTGTCTTCTCAGGCCTATGTTTGCGGAATGCA 128 NON_CODING GCAGTGTGTTGCTCAGTAACTTCCAGGACCATCCTCACTATCCAAG (INTRONIC) GAGATGATGGGATGAAGTTTTGCAAATGGCAAGGCCTGGCTCTAAT GCACAGAGCAAAGCACATCTTTCTTTGCTGTGTGAAGTTGCAAAAT GATTACACTATTTCCTTGAGGAGAACAGTTATAGACACCCAGTGTT ATGCATTAGTCAGTGTTGTATAATTGATCTTTTTTTAATCCCCTCCA TTAGCAAATAGAAGAAGATTGTGCAGAGACTGAAGATGGCATGGT GTGGTGATTGGCAGGAGACATTGTGATAGGACTCGAGTCCCAACTC TGCTACTCAGTAGCTCTGTGAGCTTGGACAAGTTAACCAACCATAG TCTCTTTATTTGTAAAATGGGGATAATAATAGACCCTATATCACAT GATTGTTATCAGTATTAAATGGAAGAACGCATGTGGAATACTTGAC ATAGAGTAAGCATTCAATAATTGTTAGCTATTAACAGTGATACTTA TTAATAGCTAACACAGTGACATATGTGTATTCAGATTCTAAGCCGG TGCACCCAGTCCTCCCTTCACAAGAGGAAAGTGTCAGCATTGCCAG AAACATTGTATGTCCTCAGTGCTGGTGGCTCCAGCTACCTGTCCTCC CCTTAGCAATTTGGTATTGTCCAAACATTTAGGTTTCTGAACATGCC TGAGGCTTA 129 NON_CODING GTGTGTGTGACATTCTCTCATGGGACAATGTTGGGGTTTTTCAGACT (UTR) GACAGGACTGCAAGAGGGAGAAAGGAATTTTGTCAATCAAAATTA TTCTGTATTGCAACTTTTCTCAGAGATTGCAAAGGATTTTTTAGGTA GAGATTATTTTTCCTTATGAAAAATGATCTGTTTTAAATGAGATAA AATAGGAGAAGTTCCTGGCTTAACCTGTTCTTACATATTAAAGAAA AGTTACTTACTGTATTTATGAAATACTCAGCTTAGGCATTTTTACTT TAACCCCTAAATTGATTTTGTAAATGCCACAAATGCATAGAATTGT TACCAACCTCCAAAGGGCTCTTTAAAATCATATTTTTTATTCATTTG AGGATGTCTTATAAAGACTGAAGGCAAAGGTCAGATTGCTTACGG GTGTTATTTTTATAAGTTGTTGAATTCCTTAATTTAAAAAAGCTCAT TATTTTTTGCACACTCACAATATTCTCTCTCAGAAATCAATGGCATT TGAACCACCAAAAAGAAATAAAGGGCTGAGTGCGGTGGCTCACGC CTGTAATCCCAGCACTTTGGGGAGCCCAGGCGGGCAGATTGCTTGA ACCCAGGAGTTCAAGACCAGCCTGGGCAGCATGGTGAAACCCTGT ATCTACAAAAAATACAAAAATTAGCCAGGCATGGTGGTGGGTGCC TGTAGTTCCAGCTACTTGGGAGGCTGAGGTGGGAAAATGACTTGAG CCCAGGAGGAGGAGGCTGCAGTGAGCTAAGATTGCACCACTGCAC TCCAACCTGGGCGACAAGAGTGAAACTGTGTCTCTCAAAAAAAAA AAAAAACAAACAAAAACAAAAACAAAACAAAACAAAACAAAACA AAACAGGTAAGGATTCCCCTGTTTTCCTCTCTTTAATTTTAAAGTTA TCAGTTCCGTAAAGTCTCTGTAACCAAACATACTGAAGACAGCAAC AGAAGTCACGTTCAGGGACTGGCTCACACCTGTAATCCCAGCACTT TGGGAGATGGAGGTAAAAGGATCTCTTGAGCCCAGGAGTTCAAGA CCAGCTTGGGCAACATAGCAAGACTCCATCTCTTAAAAAATAAAAA TAGTAACATTAGCCAGGTGTAGCAGCACACATCTGCAGCAGCTACT CAGGAGGCTGAGGTGGAAAGATCGCTTGTGCACAGAAGTTCGAGG CTGCAGTGAGCTATATGATCATGTCACTGCACTCCAGCCTGTGTGA CCGAGCAAGACCCTATCTCAAAAAAATTAATTAATTAATTAATTAA TTAATTTAAAAAGGAAGTCATGTTCATTTACTTTCCACTTCAGTGTG TATCGTGTAGTATTTTGGAGGTTGGAAAGTGAAACGTAGGAATCCT GAAGATTTTTTCCACTTCTAGTTTGCAGTGCTCAGTGCACAATATAC ATTTTGCTGAATGAATAAACAGAAATAGGGAAGTAAACCTACAAA TATTTTAGGGAGAAGCTCACTTCTTCCTTTTCTCAGGAAACCAAGC AAGCAAACATATCGTTCCAATTTTAAAACCCAGTGACCAAAGCCTT TGGAACTATGAATTTGCA 130 NON_CODING CCTGGCTGATTTCTTGGTCTCTTGCCCTCATTCACCGAATTAATTCT (INTRONIC) CTACACTGCTGCAAAACTGATCTTTCTAAACACAGGTCAGCTCATG TCACTCACCTCCTCAGAAATCTTCAGTAGCTCTTCATTAACCAACAG GGGGTTCCTAACTCCCCGTCTTGGCATTGGAGGACCTTTCCCTGCCT GATCCCCGCGATCATCTTTTCCTGCAATATTTACTCAGGCCAGTGCT CACCCCTTCTTTAAAATGCTGGTGCTGGCTCAAGAGAGGCAAACAG CCATCTCTCTCATTCTTATCTTCCCTGTCAAGACTTCACATAGGTGG ACTGATGCTAGACTATGATGATGAGTCTCCAGTGAAAGTTTCTAAG TAGAACTCTCTCAGGGTTTCTAGAAGCATTTTTGTTTAAGAAAATAT TGTGGGGGGAGCGGGATTTTTAAATGGTGGAGCTCATGGTAAACA AAATTATGTGTGCAAAATGTTAATAGAGCCTTTCTAATATTCTTGTG ATTAACTCTGGTGACAGTTGGCTGAGTGTTCTTGTTTCTGCAACGCC TGTCTTTG 131 NON_CODING CTGATTTTATCAAAGGTTTGCCAGCCAATAAAGTGCATCCCAAGTA (INTRONIC) TACAGGGGAGAAAGCTAGACTCCTACAGGGTC 132 NON_CODING TCTCAGGCATTGTTGGGGCATAAGCTCACACTGTAAGCTTTTCTCAT (UTR) GAATTCACTAGACATAACGTGGAAGGAAAACGTAGTCTTTTGGGA GTACAGGGAAGCCAGCCCCTCAAAGCTTATGGAAGACATACCTGC AATGGAAGCTGTTGCCCAATGTCTCCATTACTATCTTTCAAAAGAG AAGCCAGACCCAGCTTCAGATCAAAAGTTCTTGAGACAGAGGAAC AAAACCAATCGATTTCCAGGGAAGCTAATCAACTCTCTTTTCCCTCT ACCACAAAACTGCCCTGCTGGAGTGGTTCTGAACCTGTACCCAGGA CTCGATGTGGTCACTAATAACAATTAACCTGAACTGAGTCCACAGA ACTCCACTCGGAACTTTCTTCTTTTTTAACTAGTGGCCCAATCATTC CCACCATCTCTGTGCTGATAAGTACGTGTCCTAGATGAGAACCCTG AAGAATGCAGACCTTCTTCCCCCGAAGGAGATGCCACAAGCTCTCC AACACAGCCCCCTTTAGTTCCAAAGACTAGAGATGACCACATTGGT AGAAGTATATCTCGAGGCACAGGAAGGGAGCCCCACCAGGGATAA TTCAGACAGGACTAGAGAATAACATCATTTCACATACCCTGGGATA AACACCCTGGGTTCCTATAGAAGGACTATTACTTATGGGAGTCCAA CTTCTCCTTTTGTTTTGTTATTATCAGTTTATCTTTCTCCCACTCCAC TTTTCCTTCAAGGTACCAATCCTTTCCTGTTCCTCGTTTGGCCATCTT TCTTTTTCTGCCTCCACATTGGGAGGGGAGGACTTCTCAGTTCTAAC AAGCTGCCATACTCCTAAGAAAGCCATTTTTGAAAAATTTAACAAT CCAGGTTCTTCTGGAGAACTCATTCTCCACACGCACAGTTTGCTGC AAAAGGAAGTTGCAAGAATTTCTTGAGGAAGAAACTGGTGACTTG GTCCATCAGTCACGAAGTTCTTTCTATTCTCGTTTAGTTTTCAAGAA ATTATTGGTTTGTGTTGCTCTGGGGAAATTGGAAATCATTACATTGT AAAGACAAATATGGATGATATTTACAAGAGAGAATTTCAGATCTG GGTTTTTGAAAGAAAACAGAATTGCGCATTGAAAACGATGGAAGG AAAAAGACAATGGTCTAATGTGCATTCCTCATTACCTCTCGTGGCT TTGGCTGGGAGTTGGAAAAAGCTAAAATTTCAGAACAGTCTCTGTA AGGCTCTCTGTGGCTCCAGTTCACCATTTTATATTGTTGCATGCTGT AGAAAGGAGCTATTGCTGTTGTTTTGTTTTTTTATTTAAATCACTAA GGCACTGTTTTTATCTTTTGTAAAAAAAAAAAAAAAGTTGTTCACT GTGCACTTATAGAAAAAATAATCAAAAATGTTGGGATTTTAGAAGC TCTCTTTTTGATAAACCAAAGATTTAGAAGTCATTCCATTGTTAACT TGTAAAAATGTGTGAACACAGAGAGTTTTTGGTGATTGCTACTCTG AAAGCTGCCAGATCTTATTCTGGGGGTGGGATGTGGAGGAATACAC ATACACACACAAACATACATGTATGTATAATAGATATATACATATG TGTATATTATATCTGTGTGTGCATGTATCTCCAAAAGCGGCGTTACA GAGTTCTACACCAAAAGCCTTTAACCCTTAATCTGCTGTGAATGAT ACCTGGCCTTTCTCACTATGAATTTCTGATTAACCAACCAGACTACA CGTTGCCTCTCTGTGTATGACTAACGGCTCCAACCCGATGACTCAC AGCTACTTGCTTATCGTGAACAAGCTCATCTTGGCAATGAATATGG ATGTGAAAAGACAGAACAGCTTCACCATTAGTAGCTGGAAATGGT ATCACAGTCTCTTATAGAGGAATATGAAAGGAACAAGAAAATCAT TTTACATTCCTTTTATCTGTATTGTGCTTTAAAAGATCCACATGGTA AATTTTTTATTTTGCTTTTATGTCAGTCATCAGAACCAAAAAAATCC AGAAGAAAAAATTGCCAGTGTTTCCTTTGAAGATGAAGCTACTGGG GAAGAAAACCTTATTAATACACTCCACACATTTGTTCATTCCTCAG CTGTTGGTGTTTTCTTGGGGTCTTGACAAAGCTTGCTGGTCAGTGCA CTTTTCAGGTGTCACGTTTTGCTGTTTGTATGTTTTTTCTTCCCCTTA CTTCCTTTGGAAAACAAACTCACACAGTGCCCCTACTCTGAGACCT GGGACTGAGTGTTAATTATTTTTTCCTTGGGTATTTCTATCTGAGAG ACTAGACCTAGTTAGGAGGCCTCTGTACTTCTCCAGATTGTACCTTT TTATGGGGATCTTTGAGGCTATGACCCAGGACTGATAGATATGCCT TACGGAAGACAAAAGATAAAATGGTTCCTATATCCTAATGCAAACC AACACAGTTAAAAGAGCAGATCTCTGGATAACTGCTCTCAACCTGC TTCTACAGTCTCCACAAACCGCATTCACCCTCTCTCTTCATAGCTCA GACATGAAATTTGAGGGAGAAAACTGGAGATAATTGGGAGAAAAT TGATGAAGTTGGCTGCTTCCAGTAGATCAGATAATCCATGAATTTG TCTCCCATTGAGAATTTTATTTTAAATTCTTTTAAACTCTTCGTTGTG TCTTTTGTGATGACAAATCAGGCATGACTAAAAGATGTACAGAGAC TTACGAAGATGGTCACATTCAAGTTCCCTAATGCTCTTAGAACCTG AAGATGACCATGTGTAGTTTTCTTAAGACCTCTGAACCCCCATGGT GATGAAGACTTGAAGACATTTGCAGCTATCTGCTGCAGTCTGGTAG ATTCATACTTATCTAAAGAAGTCAAAAAATTTATTCGTGCAAGTGC TTGCAGGAAGCCAGTGCTTATTAGTAGTGACCCTGCTTCTATCAAC GTTATTG 133 NON_CODING GATCGCTGTGCTAGGTCTGACCAAAACCAGAGGGCAGTCTAGTCCT (UTR) GGGGGTAAAGCCCTCAGATCCCAGGGTACACTCTTCTCCATTCCCT CCACCCACTTGCCTGTCACCCCAGTCACCTAAGCAATCACTGGGCC CAGAGGAGAGGAGACAGACACACACTGGCTCCTGGACCTAAAGGG TATGAGCTGGAGCTAAGGCCAGCTAGAGCTTCCACTGTCAGCCCTC ACTGTCAGTCCCACTGCACCCCCCTGTGCCTGCTGGGCACTGGGCA CTAGCTAGATGCTTTAGGTTGCTTCAGCTGATCCTTCAACTCTGTGA GGTGGATACCAATATTCTA 134 NON_CODING CCCTGGAGGGATCCTAGAAAGCATTGTCATATTGCCATCTCCATTA (UTR) GCTCACTTTTAAACAACTAGGGTGCTGGAAGAACCTTTGTCTGAGG GTAGTTCA 135 NON_CODING GTACACCCTGGCAAGGCTTCTCTTCAGACTGAAGCAGCAATTCTGC (UTR) CACTACCAGCAGCAACCAGGACGTCTGTTCTTTGTGGGGGCCAGAT CAGAAGAGAGAGGCCCCTGTGACGCCCGGGCTGCTTGGTCACAAC TCTGTCCAATTCAAGGATGTTTATCGGCCTCTCTTA 136 NON_CODING GGCTGCATGGTTATCCCTCTCAGTGCAATATAGCTAAAGGGGCTTG (INTRONIC) AAATGCTGGGAGTAGTCTTAAACAGCCCATTCTTGAAAGGTTTTCA TTAACTCACTCTAAACATCTAAATTAAAAATGTTTTTGTTTTCACTA TAGTAAACAGGAGTGTAACATTGCAGGTTTGGTACATTTCTGAATG CCTCTCCACACACTGAAGCACAAGAGCCACTGAAAAAAGCTATAT GATAAATATTTTAAAAATTATTTATCTGTGTTGCATTACATGAGGCC TTATCTCCCAGACACTTAATAAAAGAGCTAATGAGAAGAAGAGCT AAATTCTAAGATTTTGATGTTTGGTCATTAAACATTACAGACACCA GTGATCAGAGAAAAAAACAGAAGAAATAATGAGAAAGTGACATA AAAAATTTTAAATGCAGCAAGATATATCAGAATCACGATATCTGGC CTTTTATTTATCTATCGGCTCACTACTACTACTACGCACACAATTTA TCACTTAAAAGAAAAATACATAATGTTGTTAGAATTTATCAGCAGT AATGCTCCAAGCTCTATCTTTCTACAAAAATTTCATATCAGTAGGTT TGCTTGAGGATTCTAGATTTGGTAAGATTGCAGTTTGCACAGAGAA AAAGATATCAATATCAATAGGAAAATATTCTTTTAGAATTTCTCCA TGGAGCTGACAACATCTTAGAATGTATCGTCCTAGACAGAGACTAT TGGAAGAAAAAACTTTCCTTATTTCTAAAATTTAAATTCAAAGTAT CTTCTGGTGGGGACGAAGAGAGAGAGAGGAGAAAGGTTGCTTGCT GTGACTGGCAGGATTTTTTGAGCAGTCTGCTGCTTTCACTCCACTAA AGAAACAAAACTTTCAGAAGTTTCATTTCCCTTCTATAAACCACAA ATCCAAAACAAAAGAAAGTGGAATAAGATAGTCTTTAAAGCTAAT CTTGGTTTTGCTAATTTGTAAGCTTTCACCAGCAGTTCTTGTTTTGCT CTGTTTTGATTTTGAGTGAATCTCATATTCCTGGCTCTGGTGGAGAA TTTTCGTGCTTTTAAAGATTAATTAATTTAGTCCTTTTTGCAATGGTT TGTTCTTTTCGGCATCTAGGAATTAAAGAAAGTGCTCAACCATAAA TAAATGTAGTTATGTCCAAAGTACCTTCACATAGACACACTATACA CAGGCGTGGGCCTTTTGGAAACACCTGAAGGCCAAATGTCTGACTG TGAGTGGAAGATCCAGAGTGTGCTGATAGAGGAAGCTTTTCTCATC CCTCGAGAGCAAAGAGGGTGATGGAGGCAAGAGTCAGAGAGCCCT GTTCTCTTCTTCATGTACACTGCAAAGGGCAACTTCTCTAGAAGCAT TAAAAGTGTCAATTAGGTTTTCAAGTAAGCGTCATTTATTCATATAT ACATTCATTTGTCTTTTTATTTACAAAATTAAATCATTTTCCCATGA ACATTAAAATGGGAAGAGAGAACAAAGAAAATAGAGTTGAATAAT AATAACATTGATTCTGGACCAGACACTGGGCTGGACAATAACTCGA GGGTTACCTTATTTATTTACACAAAGACCCGATGAGGTACACACTA ATTATTTTCATCTCCCTATTACCAATCATGAGACTGAAGCTGAGAA GGGTTAAAAACTTGCCTAAGCTCACACAACTAAGAAGTGTCCGAGC TGGGCTTTGAACCCAAGGTTTGATCAAGGGTTGTGCCCTTAACTGC CATACCATCCTGCCTCACAGATCTGGGTTA 137 NON_CODING CTCACAAATAGGAGTAGCAATTCTAGGTGGTAGGGTTGTGTACGGA (UTR) ACCCCTGGCTGTCTGCATATATCTCAGAATTACCCCAGGACCATTG TCCCAAAGTCTAG 138 NON_CODING TTCCCGACAATAAGCTCCAACGTGGGCATAGTTGAACAAGCTATGC (UTR) CTCAAAATGCCAACGCCATATGCTTATTAGCCTGTGTGCATCATTCC AGACGGGCCTAATCATTCCAGGACTGAAACCAGAATCGCTGAAAG CCCTTGAAATACATTCAATAATTCATATGTTAAAACTTGGATATCTG TTCAGCCCAAATGAAATCTTCCTTTTAAAAAACGTCTACATTATTGA AAATTGTTCAATGTGCTTTTCAGAGTGACGGTGAGAATTTTATGCA TGTATCTTGCCTGCATATTTGATATGTTACAAACTTCCAAAATTCAA GGTGCAGCGATCCACAGAACGTTGTACATTTAAGAAGTGATTCCTT CAAGCTAATTTAAAATTTCATTGAACACATGGTGACCAGGAAAACT TTTTTTCAAGCACTGTTGGAAAGCACCACAAAGCCCTTTAGAATTA ATCTGGATTTGTTTCTCAAGTTCTGCTGAAGTTTAAAAAAAAACTTT ATTATACAAATAACTCAAAATTTTCCTGTGTAAAACTAAACCTGTA GTTTTAAAACATAATCCTGTTTGCATTAGAGCTCACTGTCTTTTTGT GATGGAAACTGTGTTCGTATGGAATGACTAAAAATCTTTTATTTGG TTTGTTTCAAATTACAATTGCTGATGGACAATTTGTATTGCAGCGAG AACAACAGAATGAAAGAAATGTATCTCTGTGCGGCTATACATATAC ATACATAAAATTGATTTTTAAATTTAAAACATATGGAAAACAAAAC ATTGAACAGTTTGAATTTTGCCAAGTTGGACATTAAAGTAAAAATG AAGTGAAATCATGCATTGAAAGAAAACATTTTGTTTCTAAATTAGT CTACCATTGAGTGAGAATAATCAATATCAAGAAAGAAGACTATCTT TCTCAACTAAACAATAATATTCCAATCAGCTTGGGAAGACCTGAAA CTTGAATAAGCAGTGGAAATGCCAAATATAACAGAGGGTATGTGC TACAGAGAAGTAAAAAGGGTTTGACTTTTTATGATGGGATTTTTTTT TTCTGGGTATGTAATCTATTTTTTTTTTAAACTGGAAAGCATTTTTG TCAGTGTGAATGAGGGTCAATAGTGCAGCCAGTGGTGACATTTTTC TTTATTTTGCAAAATGCTTTTAAAACCAAAGGCTGCTCTAGTTGATG GACAGTATCAGTCTTGATCTAAATTGTAGGACACTTTTTCATGTAAC ATAACATTTGGGGATTGGGTTTATTTAGTGTAATGAAGATAATTTG ATATAAAAATATTTTGTGTATATATATATATTTTTACTTTGTTTTCTA AATTGCTGTTTGCAGTAACAGTAAGCGCAAAGCAAAATATATAAGT TATGACTGTATGATCAGATGAAGTATGAGTTCTTTTGGTTTGCATCC TTAAATAGTTAGAGATCTCTGATAAAAACTTTGGAATCTTTGCAAA ACAATACAAAAATGCCAAAATGTGAGCATGTCAATGAAAACTAAA GACAAATACTTCACTCTTTTTCATACTATTATAAGTTATTCTGGTAT TAAATATGTTAATAAAAGTGTTTTTGTTTTGACATATTTCAGTTAAA TGAATGAATGCTGGTTGTATTTTATTTGAATGAGTCATGATTCATGT TTGCCATCTTTTTAAAAAAATCAGCAAATTTCTTCTATGTTATAAAT TATAGATGACAAGGCAATATAGGACAACTATTCACATGATTTTTTT TAATACCAAAGGTTGGAAGATTTTATAATTAACATGTCAAGAAGAC TTTATAGTAAGCACATCCTTGGTAATATCTCCAATTGCAATGACTTT TTAATTTATTTTTTCTTTTGCTGCTTTAACATTTTCTGGATATTAAAA TCCCCCCAGTCCTTTAAAAGAATCTTGAACAATGCTGAGCCGGCAG CTGAAAATCTAACTCATAATTTATGTTGTAGAGAAATAGAATTACC TCTATTCTTTGTTTTGCCATATGTAATCATTTTAATAAAATTAATAA CTGCCAGGAGTTCTTGACAGATTTAAA 139 NON_CODING GTCGCCTTCCTATGTATGACGAAACAAGAAACAGAGATTTCCAATT (UTR) GCTCTTTTGTCTTCAGACATTTAGTAATATAAAGTACCTATTTTTAT GCTGAAATGTTTATACAGGTTTATTAATAGCAAGTGCAACTAACTG GCGGCATGCCTTGCAACACATTTTGATATATTAGCCATGCTTCCGG GTAAAGGCAAGCCCCAAACTCCTTATCTTTTGCAGTCTCTCTGGGA TCAGTAAAAGAAAAAAAAAATAATGTGCTTAAGAAGTGGGACTGT AAATATGTATATTTAACTTTGTATAGCCCATGTACCTACCTTGTATA GAAAAATAATTTTAAAAATTTGAATGGAAGGGGGTAAAGGAAGTC ATGAAGTTTTTTTGCATTTTTATTTAAATGAAGGAATTCCAAATAAC TCACCTACAGATTTTTAGCACAAAAATAGCCATTGTAAAGTGTTAA AATTTACGATAAGTATTCTATTGGGGAGGAAAGGTAACTCTGATCT CAGTTACAGTTTTTTTTTCCTTTTTAATTTCATTATTTTGGGTTTTTG GTTTTTGCAGTCCTATTTATCTGCAGTCGTATTAAGTCCTATTG 140 NON_CODING TCTCAGCATATGTTGCAGGACACCAAAAGGAAGAAAACAATCAAG (UTR) CAAATAAAATAAACAGTCAAACAAACCAGGAGTTTAAAACAACAA CCCCAACAACAGAAGCCTTGGCAAAGAGGAATAAGTGATCAGCAA GTGAACACACTCTATGTCAACTCTCCTTTTATCCAGCTGAGATTTAT GGTAACTTATTTAATTAATGGTCCTGTCTGATGCATCCTTGATGGCA AGCTTCAAATCTGATTTGGTATCACCGAGGAAACCTTGCCCCCATC ACTCAGCATTGCACTTAGATACAGAATGAGTTAGATAAACTTGGCT TGTCTAGAGACCCATGTCATCTTAACCTAAAGGGAAATCTTATTGC GTTATCATAAAATTGATGATATCTTAGGGTCAGAATTGCCCTTTTTT TTTATTTTGAATGGGAAGTTCTCACTAAAACAATCCTGAGATTTCTT AATTTCATGGTTCTTTAAATATTATAAACACAGAGTCAACATAGAA TGAAATTGTATTTGTTAAAATACACACATTGGAGGACAAGAGCAGA TGACTACTTTTCGAAGTAATGCTGCTCCTTCCTAAAAGTCTGTTTTC AATCCTGGTAATATTAGGGGCACTGCGGCACCTAAGAAGCCTTAAA TGAGAGCTAATCCAATCTAGAGAGCGATGGTGTCAGCATTTCGGTC TGCATA 141 NON_CODING CAGGGCATGAGACATTCAGCGTAGAGGTTAAAACGAGGGCCCTGG (ncTRANSCRIPT) GTTAGGAACCCCAGCTCAGTTCTCAGCTCTGTACCCTTGGAAAATT CCCTTCCCATGGAGCTTTGTGGATGCACAAGGACTTGCACA 142 NON_CODING GTGGCTTGTTTACGTATGTTTCTGGAGCCAATT (ncTRANSCRIPT) 143 NON_CODING CCCAAGCCTGTCTAAGGTTACTGTGTATTAGACAGGGCCGAACTAG (UTR) TGTGCTGAGCAAAAAGAATTGAAGCAAATTGTATTTACTTAGCCGC TTCTGGGAGCCACTTCAGCCTTTCCCCTCCCCTCCACTTCTTGGGTA ATCTGACCTGAAGCATAGTCCAGGAGCAGAGTTAGCCAGAAATGC CTCCTGCTGCCCCAGCCTTAGAGAGCTCCCATCTCAATCATTGAGC CTGAAGGCTTCAAGCCCAAGAATGCAACAAGACCCCCAGCCTACA TTTCTCAGCTCCCCTGGAGCCAGCTGATCCTGTAACGCTGCTGGAG GTCAGTCTGAGCTACCAAGACTGTCCCTAGACAAAGGTGGAGTCCC CCACACTGCCCAAGACCAAATCCCTCACTCAACCTGCTGAGGTGTG GATGGGGAAACAGAGGCAAAACTGAGGCACCTGATGCATTCAGCC TGCTGTGCAGCAGTGCCATTGACTGCCCTGATGTTCAGAGAGAAAC GCACACAAGGTTTGCCCATGAGAATTGGGGAGCAGATGGCCAAGC AGATAGGTTATGTCTGTTTTCTGAGTGATGAAGTCAGGAAGCCCTG TGGCTCTGGAGGCCACTTGTGGTTCATTCTTTTCCCATATCCTTGGC TTTTAGAAATGGTTACCTTCAGGACAGTGCAGCTGCATTTATCAGA GCACTATTGCTAAGTTTTCTTTTCTGGCTTGTGTTTTTCTGGGACAG TTTAGAATTGGGAGGCCTATTCTCATAGAACA 144 NON_CODING CCTTCAGAAGCATGGGACTACCTCCCATCTAGTTCTCGTTTCTAAAC (ncTRANSCRIPT) CTAGGGGAGATGCTATCTTTGCTGCAATAATCTTAGCCTACATCTTG GAATGGAAATGGCCTTGGTGGAAATGGTCTTCAACTCCTCTGGTCC AAGCTCAGGCCCTGTGACCCTGGAACAATCCCCTTCCTGGTCCTCC ATGTAGGAGCAATAACATTCCCTTGCCAGCAGCACCAGCCATTCTG ATGATTAAATGGTATCGGACTCTGTTTTCCAAACTCAGTCATTCAG ATGCCCCCTATTTTATTTCTTCCATGTCTGCAAATGATTATAATATT TTTAAATGTAGGATGAGTCCTTTTTATTACACATAGAAATAGCTACT GTAAATAGCAAACTCTAACACTGTGCCTAATTAGGAAATAAAGGTA ACCATAAATACAGTAAAAATGAAACAATGTTATTATGGTTTAACCT GATAGTGTGGCTTGCAAGGCCCTGGGCCTGAAGCCTGGGCAATAA GTGAGAGTTAGAAAGGTGTCAAAGACATGATAGCAGCAAACTGAG GCTTTGTACCCCACGGTAAATAGGACTGAAAGCAAATTCACAGGG AGCAACTGATCCATTC 145 NON_CODING GAGTGGCCACTTGATTAGAGACCTAGCACAGGAGGAAGAGATGGG (INTERGENIC) CAGGGAGAGTGACGGGGAGCAGCACAGTCCCTGGGAGCCCGAAGT GGGTGGGCACAGGGCTCCCTAGGAGAATGGAAGGACATCTATGAG CTGTAGCCCAAGAGGAAGAGGTCACTGGGGCTAGATGCGGCAGAC CCTCGCAGGCTTTGGGAAGGGCTTCAGAATTCAGCCTGAGGGCAAT GGGGAGCCCTTTTGGGATATTAAACTTGAGTAAGATATGAGCATAT TTGCATCTTGAAAAATCATTATGGGAAGATGGCTGGGAAGAGAGG AGGAGTGGCAGAAGAAAGATAGGTTGGAGACAATTGATTGCTCGA TGATATAAAATGTTAAGTACCATGAATGATGCTGTTAGGCTGGAAT GCGCCAAGCATAAAGGTGGGGCATGGCATCAAAAGGTAGGTCAAC ATATTAAATAATTCCATGTATTGAAATATCCAGAAAATATATAGAC AGATCTATAGAGATAGAAACTGGTCTGCCCAGGACTAGGGGTTGTC TA 146 NON_CODING CACTGGTCTGCCCTTCCTAAATTAAGTATGCACTTCAATTTGATGAG (ncTRANSCRIPT) TGGAAACAGTCTATCTGGGCAGTAACCAGGGAGCTTTGTGCCTAGT AGATTGCTTCTGTTCTGCACTTCTTTGGTTTCCCACCTCAATGTAAA AAATAGCTAGCAATGAAGTCCAGAAGTTGTCAATGGTTCATCCCCA GAAGAATGCATAATGTCCAAAGTTGTATGTGTATGATGTCTTCAAT GGTATTAAGTTATTTCAAATTCTTAGTTCACCTACATAAATCATTTC TAACAAGCATCTTCTTAACCAACTTTATGCACAGTGTATGTTTGTAA GTGCTTCTGCACGAATGTTTATACATGACTGTTTCCATAGTACTTAT GTTTTTAAAAATATTCAGTCATTTCCTACTATAATCCTCATGTATCC ATGTAACTGACTCAAAAATACTTCAGCCACAGAAAGCTAAAACTG AGCAAATCTCATTCTTCTTTTCCATCCCCTTTGCATGTGGCTGGCAT TTAGTAATGATTAATAATATGGCCAGCTGAATAACAGAGGTTTGAG ACACAATTCTTTCTCAAAGGAGTCAGCTAAGCTGGGTCTACTTATG GACAAACATCTAAATGTGTGGAAGTATCTGATATTTGACAATGGTA AATTTCCACTTAGCTAGCTAGCATTGTCAGACTTCAATCTCCTCATG GCTCTGGCCGTCCTGTTTTAAGCATGATAATTGTTGGCCACATCTCA CATAGTTCTC 147 NON_CODING AGTTTCTAGTTGACTTCCATCTGCAATAAATCATGTACAGGATGAG (INTRONIC) GTAATATACTACAACTTATGTCTATTGACTTAGGATTTTATCTTTAA GAGGATAGATCCTAGATGTGAATAGCTAAGGAAGTTTGAGTGTTTT CTCCTCCCTTGCTTTCAAATAGCTTTGAAAGATCACTTTTATAGTGC ATGATAAATAGCTACATATGAATAATCTGATGGCATTCTGTAAGAG TAACAGTGCTTCAAAATCGTAACCTGCTGGGATGTTTTGTTACATG CCATCAAGTGTGATTGTATTCATGGAATAGTGTTTACTGTTGCTCAA TATTGTAAAGGAAATAAAAGATAATTCCCTATCTGAGGGGAAATTT CTCAAATATTTTAATTAAAAGGTCCCTACAGTTACCCATATAAACC TTAGTCAAATAAGATAACAAATTTTCTTGATCTCCTTTAAAAATTCT TTTATGTATAAAAATAATTATATTTATTAAAAACTCCAACAGTACA GAATTATTTGGAAAAAAAGATAGAAATCTACCATTCTCCTATCCAT GCCTGAGAGATA 148 NON_CODING CGGAGAGCCCTCTTGCATGAGTTTCGGCTTTGCCAAGATTCCAGGG (INTRONIC) ACTTGAGGACAGCTATTGAGTTATGGTTACGTGACTGCCACATTGG GGCTTGGAGGCATCTGGCAGATGGTTGGGAATGGGCTGGCACCAC ACTAATTAGGCCACGATGATCCAGTTTGACTCAGGGAAACCCAGAA GTCATAGTGCTCTTTGCAGAATGACACAAGATGTCAACATGCTTTG TTGTGTACTTTGAACAGGGATTGGTTTCACAAGCTGAAAAGTTGAA TCTGTCACATGTATGCAGCATAAAATCACAGCCGTGAGAACATGTA TACAGCAGGAAGACAAGCGACTGAGCTAGGCACGGCTGACTAGCT CTGAGCTTTC 149 NON_CODING AAAAGCCCTCTCTGCAATCTCGCTTCTCGTGTCCGCCCCGCTTCTCT (UTR) TATTCGTGTTA 150 NON_CODING AGGCTATCGGGAAACTCTGGTCCAGCCACAGTGGTCTGGCCACACA (INTERGENIC) GGGAGCCATGTAGAGACCTCCATCTCCAGCCAGGATGACACCGGTC TGCGGTTCCCAGCTCGTCGTCAAGATGGGATCATCCA 151 NON_CODING CTGGGATCTGCCAACGAAGATGAGCTCTTGCAG (INTRONIC) 152 NON_CODING CTCGGGAAAGGATCATCGCCGTTGAAATGAAAAGAGAGACAGAGA (UTR) GAAAAAAAAAAAGAGAACCCACATGAAGCTCTGAAACCAAACAGC ATCCTGCCATGAGCTTCCCAGAGACAGAAGAGACTGGAGCAAAGT CGGAAACACAGAGAAGCACGGCTTCCCCTCAGCACAGACCCTCCA GACTGGGTCTCAGAGCCGTGCCACCCACCCTCCCACACAGCCGGCC ACAGGGAGAACTGGTGCTAACCAGGGTGCTTGCTTTGGTCACGTTC AACGCACTACAGAGCTACGACACAGGGAAACC 153 NON_CODING TGTGGTACCCAATTGCCGCCTTGTGTCTTGCTCGAATCTCAGGACA (UTR) ATTCTGGTTTCAGGCGTAAATGGATGTGCTTGTAGTTCAGGGGTTT GGCCAAGAATCATCAC 154 NON_CODING TGATGGGCTAAACAGGCAACTTTTCAAAAACACAGCTATCATAGAA (UTR) AAGAAACTTGCCTCATGTAAACTGGATTGAGAAATTCTCAGTGATT CTGCAATGGATTTTTTTTTAATGCAGAAGTAATGTATACTCTAGTAT TCTGGTGTTTTTATATTTATGTAATAATTTCTTAAAACCATTCAGAC AGATAACTATTTAATTTTTTTTAAGAAAGTTGGAAAGGTCTCTCCTC CCAAGGACAGTGGCTGGAAGAGTTGGGGCACAGCCAGTTCTGAAT GTTGGTGGAGGGTGTAGTGGCTTTTTGGCTCAGCATCCAGAAACAC CAAACCAGGCTGGCTAAACAAGTGGCCGCGTGTAAAAACAGACAG CTCTGAGTCAAATCTGGGCCCTTCCACAAGGGTCCTCTGAACCAAG CCCCACTCCCTTGCTAGGGGTGAAAGCATTACAGAGAGATGGAGCC ATCTATCCAAGAAGCCTTCACTCACCTTCACTGCTGCTGTTGCAACT CGGCTGTTCTGGACTCTGATG 155 NON_CODING TGGGCCTGTCGTGCCAGTCCTGGGGGCGAG (UTR) 156 NON_CODING CCCGCCAGGCATTGCAGGCTTAGTCGTGGCTACTGTTCTCCTGTGCC (UTR) GCTGCATCGCTCTCTCCCGGGAAA 157 NON_CODING GGCGGCTATTCTAAAAGTGTCTTTCTATCACTGTTAAGGGGGGGGG (UTR) AAAGTGAGGTTCGAGGATGACGTAGGTAACTCTCCCCTCCCAAGTC CATGTTCCAAGTGGCTATGTAAAGCAAGATGATACAGAAAGCTGCT CTAAAATCTCACTGAGTGATTTCACCTTCGCCTACTATGAAATGTCT CATCAGACCTGACATGTCTGAGATAACCAAGGTGATTCAGGATTTG ATCAAAAGAAGTCTAGTAAGAATTAATTACACAGAAGCCTCCTTTC ATTTCTATGGGCCAAACAAAGGCCATGGATAACCCTACCCGCTTTA TGTCATTACCCATTGGGAAACACAATGGCTACTTCTGTTAGGGTAC ATTGACCTTGGTCAAGCATCTTAAAGAAGGCAACCCTAATTGAGAG CTGTCTTGGCTAATACTCTGCACCACAATTGTGATGTCCTAGTCCTA CCACTAGAGGGCATGGTACAGCCTGGCAAAAGTTAAAAGGGGTGT GGCAGCTCCCATCAGGTCTGGAGGTGGTCTATAAGCACAGTTGACA GTTGTGCATTGGGATGGGTGGAGAAAGACGACAAGAGAGCAGAGA ATCTGCTGATGTGGCTGCGCTTACTTTTAGTGACTTTATGTACTTAT ATTAACAGCTGGAAATAGGTTGTTGGGTTTTGAGCAGGCTGTTATA GTGAGGAATGTTCATTTTTAAATGTTCCTAACAGATTTTGCTTTTGA AAAATGCTTGTTACATGAATAATTTGTGGACCAGGGATTGCTTTTCT GAAGGCAGTATAGGGAACATGAATATTCAAGATGAAATACAAAAA TTATGTTTAAGGGTCATAGTGTATAAGTAGCTTCCTAGGAAACCCT TTGTGTATCTTTTCAGACTGGGGTGGGGGCTGAGCATGCTTGTGCA GAAAGAAGCCATAGCCAGAAAGGACAGAATCTCTCCCCCACTCCC TTGCCCCATAACCAAACATAAGCTAGCTAGTCTTGTCTAATAGATG GGATTTACTATAGGTGAAGATAGCCCTCATATTCAAGGACAGAAGC TCTGGCAGGAGTAAATTAGCAAAGCAGAAATAGTACCCTTTCATTC TTGGAGGTGCTTTGAAATTTTAGGTAGAATATAATCGAAATTATGG AGGTTCCTTAGTGCTCAATAATATAAGACCTGGTGTTATTAGAACG AGTCTTTCTTATAAACTAACAGAGCAGGTATATGCCTGTTAGACCT TAGCTGTGGGGTTCCTTTACTATTGGGTGAATCATTAGGTATAAAA AATAATCATCAACCAGGCAAATTACTTTGCTTCCTAGCTGATGTCA TCCCACATTGGTACAGGTGTTATTCAGTACTGGGTGGTTCAGCAGG GAAGCCGGGTGGGACCAGTGTGTCTGTCATGAAACCACTAACTGCA TTCCTGACTGAAGAGCCATCTG 158 NON_CODING GTGAGGGTGACGTTAGCATTACCCCCAACCTCATTTTAGTTGCCTA (UTR) AGCATTGCCTGGCCTTCCTGTCTAGTCTCTCC 159 NON_CODING TGTCCATGTGCGCAACCCTTAACGAGCAATAGAATGTATGGTCACC (UTR) TGGGTGTGGCCAGTGCCCGCTGTGCCCTGCATGATTCTGTGTTGCC GCTGCTGCATAGTTCCCAGCCCCATCCTGTCCTGCTCACTCATGGGG GCTTCCAGACCCCGGCCCCACCAGGGCTTGTGTCATAGGGAGCCCT TTGCACTCCTCGTGTGTTGGCAAACGCA 160 NON_CODING CCCTGGCAGGCTCCTTCTAAACATGCCTGTTGACCTGGAGCTGGCG (INTERGENIC) CCACCAACTCCAGGGCCTTTCCAGGGCCAGACAGGTAACACGCATG AACCCGAGTGACAGCTCTGACGGGCTGTTTCGGTGTCAGGAGACAA AGCTGGCAGGGGCAGGGGTGAACTGGAGGCAAGTCAAGTCACCTG TGGCCTGTGGGGCTGAATGTGGGCCCGGTGTTGCCAGATCCTTTGT CATAAGAAGCTAGAAATCCAGATTTTATGTGTGTGTAATTTGTAAA TGCTGAAAGCTAGCCTGAATTTTTTTTTTTTTTTTTTGAGACAGAGT CTCGCTCTGTCGCCCAGGCTGGAGTGCAGTGGCGCGATCTCAGCTC ACTGCAAGCTCCGCCTCCTGGGTTCACGCCATCCTCCTGCCTCGGCC TCCTGAGCAGCTGGGACTACAGGCGCATGCTACGACGCCTGGCTAA TTTTTTGTATTTTTAGTAGAGACGGGGTTTCACCGTGTTAACCAGGA TGGTCTCGATCTCCTGACCTTGTGATCCACCCACCTTGGCCTCCCAA AGTGCTGGGATTACAGGCGTGAGCCACCACGCCCGGCCACTAGCCT GAATTTCAATCAAGGGTTGGCTGATACTGTGTGTCCAGGGTGGACT GGATTTGTCCTGGGGGGTTCTCTGGTTTGCTGCCTCCTGACCACATG ATGGGGCCTTCGAGGTCGAGGACAACTGTTCCCATTAGATTGCACC CTCTGCCCTCAGGTTCTTGAGGGTGTGTGGACACAGAGGCTTTCCA TGGGATGTCCCTGAGCCGGCCCTTGATTGGGGCCTCACCATTTACA GGGCCGTTTTATTCTGCAAACCGAAACTTGGGTCATGTGACCTGAT GGGATTATGGGACTCCCTCCAGGTGCCCGAGACAAGGTTGATATTT CCAAAATATTTTGGTGATTTAGTGGGACAAGCAAATGACAGAATAC CGGAGAAGGCAGGGATCGTGGGTGTCAGGAGCCAGAGGGGAGGG GGACAGATGTGCTGTGTACAGGACAAGGTGTCAGGTGACTCCTTCC CAGCAGGGCCTCGCAGATGCACAAGCACGGAGCTGGTGGGTTTTG CCCAAGAAAGGTCACGCGGCACATG 161 NON_CODING CTGTCGCGATGGAGAAGTACTAAAATCTATGAAAGAGTTCTAATGT (INTERGENIC) AGATTTAAGGTCATGAGAAGTCTCCGGCAAAGTGGCATTTTAAAGT AATCCCTCAGTCGTGGAGCTACTCCAATGAGAAGCCTGCCACTCCA GGGCGCACCACGGAGGAGGATCCCCAGACAAGAAGACCTGGCTCC CCAGAGGAGTGCGGAAAGCCAGCATGGCTAGAGGACACAGAATGA GGGAGAAGACGGATCCGATCGCAGGCATCGGGAGTGCTGATTTTTC TCCTTTGAAAAACAGGTTGCCATCTACCTTTTTAAATGTCCCACTGT GTAGGAAAACTCTGGGGAAAGCTACGTCAGCAATA 162 NON_CODING CAAGCCGAGATGCTGACGTTGCTGAGCAACGAGATGGTGAGCATC (UTR) AGTGCAAATGCACCATTCAGCACATCAGTCATATGCCCAGTGCAGT TACAAGATGTTG 163 NON_CODING TGTGGCCCACACGTCATCCGATGCTGCGTGCTCACACTTCACGGCA (INTRONIC) TCTCCAGCACCTGCTAGGCCATGCGTGTCCCTTGGTGACGCCGTGG GGTAGATCCCTGATTTCAGTGGCCCTCATTTAAAGTACACGTGCAA GTCAGACTGGGAGAGCCCCGACGGGACAGTCTCGGTCTGTACCTGC ACCTGCCGTGCTGTGCTAGGCGGGTTTCCTTCCTGTGAGAGCTTTTC TCACTGTTCACCAGGGACAGCAGTCACCTTCCTAGGAGTTCACAGG CAGTGCGCATGTGGGAGCGGATCTGGGGAGACCTTCATTGGCCGCC TCTGATGTCCGCAGTGTGTCAGGTCACCAACA 164 NON_CODING TACCAAGAATGCTGTCAGGGTCATTGCCTACAAACTGATGATGCTG (INTRONIC) TGCAGAATTGCGCCTCTACTGTAAGGCTTTCCCGGTCCTACTTGGCG AGTCTTAAT 165 NON_CODING CCCAGAAGGCAGCCGTATCAGGAGGTTAG (INTRONIC) 166 NON_CODING AACTGAGGACGCGTGGATTCTACTCAAGCCTCCAAGTAGTGGCATA (UTR) TCAGTCTTGGAGCTCCTAGCTGGTGATACGGAGAGGGCTTTGGAGG ACTTGGGACAGCAGGGCCAATTTTTTTGCCCAAGTGCCTAGGCTGC TAACTCA 167 NON_CODING GATGGCCACGCAGATCAGCACTCGGGGCAGCCAGTGTACCATTGG (INTRONIC) GCAG 168 NON_CODING CACAGCGGAGTCTGTCCTGTGACGCGCAAGTCTGAGGGTCTGGGCG (UTR) GCGGGCGGCTGGGTCTGTGCATTTCTGGTTGCACCGCGGCGCTTCC CAGCACCAACATGTAACCGGCATG 169 NON_CODING TGCTAGTCATGCACCTCAGACAGTGCAAGGTGCTTCCTTTGATCTAT (INTRONIC) CATGTCAGCAGTGGGAGAGGTCCTTAGCCTAACAGAGGTCTGACTA AAAGAACAGCCTTCAAAGTGAGTGTCATTTTCAGAAATAACCATGC TCTGCCAGATCTGTATGGGGTTTTTTAATCGCATGCTGCTGACAGA ACGTTTC 170 NON_CODING CGTGCTCATCGTCCATAGTCCCATATTTTCTTATAATAAACAGTAGT (UTR) ACTGGCAGGCACAGTAGGGGCACAAGGCATCTGTCTTATTCAAGAC AAGTTTGAGACACTGGAAAAAAAGATACTTGTTGTGTGTGTTGGAC AGAGTGGCGAGGCTGAGCACTGTCACAGGGGCCTCCCATGTTAAG AGGGACTGTGGGGATGATGTCAGAACAAGACGTGGTGGATTTGAG GTTGATCGAGTATTAATACTACTGCCTCTCCTTGTCTTAGTGGGTAT TTAAAATAGTAAATAAGAGAGAGGAAGGAGGTGACGTTCAGGTGC TGTGGGAAGCAGGCTTGGCGGAGGGGTATGATGATGAGACCCTCA TTGTTCACTGGCTCCATCGCACTCCTCCCTGGGGCCGTGTGCCTGTT CCATTCTTCCCACCATTCGAACTGAGCGAATCTGGCAAAGGAGACA CGTCTGTGGGAATGCGTAGATTCCGCCTCGGAAGAGAGCTAGCGCA ACACTAAGAAAAGCAGGCTTCTTGTTTATTCTCAGGACCTTTTTGTA ACAGGGCTACATTCTGCAAACTGCTTACAAAGGAAGACTATACGTC TTAACAAATTATTTAGCCACTGAGTCCTCCCGATTCGGACCTGTTTT AGTAATGGCAGAAGAATCCCTGAGCAGGTTCAGGTGCCCTAGATG ACTAGGGTGCTGAGCTCTGGCGCCTTCTGTCCCCACTCTTTGCCTCC CCGCCCCTTCCCTGAGCCACCCCAGCAAGTGGGTGTCTTTTCTCC 171 NON_CODING AGAGGGCTGCTCAACTGCAAGGACGCT (UTR) 172 NON_CODING TCTGGGGTCACCGAGAAAGTCTAAAAACAGGAGGCTGAAGGTACT (UTR) GTGATGGCTTTAAAAATGGCCACCTTATTAAATAGGGATTGTATCA ATATTGAAATGAAGACAATCTTTCCAACTTTGGGTGTTTCACTTGCT GTTTTAATTGTTTGTTTTTAACACTTTGTAGGTTTGTGTTTTCATAAT CTTTAATTTGAAACTCATGTGTCCTCATGGATCGTGGATGCCTTCAT TTCTTGAGCTCTCAATGCAGACATTTAAATGGCTGCAATCAGTAGA GTGACCCGCGGATGGCATAAATGCACCTCCTTTTCTTGGCCTTGGA TCTATGGGTCTGGGATTGTGGTCATCTCCTCAATCCTCAAAAAGAG GCTGAATCAATGTGGCCGTGGGTGGGAACTTACATACAGAACCCA ATGAAGAACTTGACTGTCTAAACAAGGGGGCCTCGCATGGAGCTGT AAAGCATC 173 NON_CODING CCTGGCTGAGTCTAGACGTCTGATAACCACGTAGGTGGGTAAGGTA (INTRONIC) ACCACTGGGATGGCTGGAAGGTGTTACCCAGGGAAACTGAAGGCC AGGATGAAAATAAAAGCAAACGGTTTCCCCTTGGGCAATGACTGC CATCAGGATTCTGCTGCTGATAAAATGCTGCTCCTTTGTTCTGCTTC CTGCGTGTTCATCCATATGATAGCTGTTAGACATTTCATTCAGCTTT CACCCACCTGGCACTGCTTCAGTGCCAACCAACGGCAAGGTGCTCC CCAGCTGCCATGGGGAGCCGGGTACAAATAGACCTCAGCGAAGCC CTGCGTGCATGCAAACTGCGTTTGCCTTTTGCATTCTGCTTTTCTCT CGGGGCCATGCTTGGGACACTTACACGC 174 NON_CODING ACAATGGTGTCTTCAGCGGCCGAAAGGAGGGGCAGGGGAAGCCCC (INTRONIC) AGCAGCAGGAGCAGGTGTGTGGCAGCCCTTCACAAGGGGCTTTCAT GTCTCAGTTGTATGTTGCCAGTGTCACTT 175 NON_CODING TCCCTGTGTAGGATGGCTTCCCGTTATTTTTTTTTTAAGCAAAGTAA (UTR) ATGAACATCAAATTTCCATAGTCAGCTGCTGTCTTTCTGCCCACTGA GAGCTCTTTGGTGAAGGCAAAGTCCTCCTTCTTCATTAGCGGTCTCC CATGTGGGGCCACATCTTCCCTCACCAGGAACCCAGTGGGCGCGCT CCAGCCCCCCTCAGCTTGCCTTTTGCGTGGTCATTAGAGCTAGGGC ACACGTCATGCTGATTC 176 NON_CODING TGGGGCCAAGACATCAAGAGTAGAGCAG (ncTRANSCRIPT) 177 NON_CODING TTTCTCACCTTGCTGCGGCCTGCTGTTTGGCAGGACGACTTGACTGG (INTRONIC) CTGCGCTGTGGTTTCTGCGCCTGTGATGGCTCCTTCTGAATGCCCTC TGAGC 178 NON_CODING TAGGCCCGTTTTCACGTGGAGCATGGGAGCCACGACCCTTCTTAAG (UTR) ACATGTATCACTGTAGAGGGAAGGAACAGAGGCCCTGGGCCCTTC CTATCAGAAGGACATGGTGAAGGCTGGGAACGTGAGGAGAGGCAA TGGCCACGGCCCATTTTGGCTGTAGCACATGGCACGTTGGCTGTGT GGCCTTGGCCCACCTGTGAGTTTAAAGCAAGGCTTTAAATGACTTT GGAGAGGGTCACAAATCCTAAAAGAAGCATTGAAGTGAGGTGTCA TGGATTAATTGACCCCTGTCTATGGAATTACATGTAAAACATTATCT TGTCACTGTAGTTTGGTTTTATTTGAAAACCTGACAAAAAAAAAGT TCCAGGTGTGGAATATGGGGGTTATCTGTACATCCTGGGGCATT 179 NON_CODING AATAAGAAAGGCTGCTGACTTTACCATCTGAGGCCACACATCTGCT (ncTRANSCRIP)T GAAATGGAGATAATTAACATCACTAGAAACAGCAAGATGACAATA TAATGTCTAAGTAGTGACATGTTTTTGCACATTTCCAGCCCCTTTAA ATATCCACACACACAGGAAGC 180 NON_CODING GCTGAGCCCTAACTGATACGCTGTGTTTCCAGTGTCCCTCATCCACT (INTERGENIC) AGACTCAGTGGTGTCAGGAATGGTGTGGTATTTTGTTATAAATTTA ACTCCTTAGATGGACACACAGAGAGCCTCGATAAATATTTTTAATC CATCAATGCAAGGAGTGTGGTTGTCAGAAGTCAGCTAAAAGTCCA AGTTTAAATCTAAGCTCCGCCGTTCACAGCTTGGGTGACCTCAGCT TCTTTTTTGGAAATGAAGTTCATATTTTCCGAGCACTTTTTCTGTGC CAGGTGCTTCCAAATGTATCTCGTTTAATCCTCACAACATACCTCAG AGGAAGACATCATTTTTACAAGTAAGGAAATAGAGGCTCAGAGAG ATGAAGTGGTTGACCCGGGCTGTCTATCTTGTAAATGGTGGGCTGT GATTCCCACACGACTGGAGTTT 181 NON_CODING TTGGCTTATCAGTTGGCATGACCTCTGAAGATCTTTTTGCTCTGAAT (INTRONIC) GTTTTAATCATCAAGTTCTGGTGGTTATCCAAGGTGATCCTAATCTA CTTTGGGGTGGAGGGAGGAAGTGGTGTCAGGAGAGATCAAACCAG GCCACCTTGAGCTGAAAGCTCTGAAGGAGAAGGATTCCTTGAAATG GAGGTAATTTTTGAATTATAATAAGTGAGAAGACTGCAAGGGAGA CAAGCTGAGGGACAAATGCTCTGTGCTTTTCTCCTCACTTTCACAA ACAGGAGGAGAACTTCCACTGACCTAGCAGTAGTTTGCTCCTCCAG GCTGTCATGTCTTCTGATCATGTCTTTTATGAGGTGAATTTCTCCTC ATGAAAGACTAGACTTTAAGGAGAGATTCTGTGCAGGTCCCTACAG TGTGGAGATGGATTGATTGGGCCTACAGATTGCAGCTAATC 182 NON_CODING GCGTGCATGTGCGTTTTTAGCAACACATCTACCAACCCTGTGCATG (UTR) ACTGATGTTGGGGAAAAAGAAAAGTAAAAAACTTCCCAACTCACT TTGTGTTATGTGGAGGAAATGTGTATTACCAATGGGGTTGTTAGCT TTTAAATCAAAATACTGATTACAGATGTACAATTTAGCTTAATCAG AAAGCCTCTCCAGAGAAGTTTGGTTTCTTTGCTGCAAGAGGAATGA GGCTCTGTAACCTTATCTAAGAACTTGGAAGCCGTCAGCCAAGTCG CCACATTTCTCTGCAAAATGTCATAGCTTATATAAATGTACAGTATT CAATTGTAATGCATGCCTTCGGTTGTAAGTAGCCAGATCCCTCTCC AGTGACATTGGAACATGCTACTTTTTAATTGGCCCTGTACAGTTTGC TTATTTA 183 NON_CODING CCTGCCATGCCGCTGCCACCGCGGAGCCTGCAGGTGCTCCTG (INTERGENIC) 184 NON_CODING GCTCACTGTCTTAGGCCTCGTCTTGGTTCCTGCATGCTCCACCTGCC (INTRONIC) TGTTCTGGTCTCTAAACTCAATTGAATGACTTGATGTTACAGCTTTC AAGCAGAGAAGTGTGGGGTGATGGTGGCAAGACAGAGGGGCGCCA TTACTCTCATCGCTCCTTTTGTGGTGGCAGTCGTATTCTCCTCCTGG GGTTTCTCTTGTGTTGGCGAGTGTATCAAAGTGAAGTGTGTTTCCAT TGATTCAGTAACTGTTGAGTGTGCCCTCAGTGTGGATGGCACCAGC CCAGTGGGGTGCACTCCTCAGCATTCGGGATTCTTCCTTTTGTCCCT CTGGGGCTTGCACACAGGCAGGCACACTCACGTGGAATC 185 NON_CODING TTTGTGTGCACCCAGTGAGAAGGTTTATTTTGACTTTATAGATGGG (INTRONIC) ATATCTAGAGCTGGAGTCCTATATTCAG 186 NON_CODING AGCCCTGTGCCTGATTCTTATAATAAGTACATATATAAAGTAACTA (INTRONIC) TAATTTTTATTTTAATCCAGTTAAATGGCTAGCAGAAGGCTTTGACC AATGGACCTGGGCATCCAAAGTTACCACATTTGTTCCTGGGATTGT AGAGATGTAGAGACCAGGTTTTGCCAAACAAATCCCAAATATGGC CGGTGCAGTGGCTTATGCCTTTAACCCCAACACTTTGGGAGGCTGA GGTGGGAGGAATGCCTGAAGCTCAGGAGTTTGAGACCAGCCTGGG CAACACAGCAAGACCCCATCTCTATAATTTTTTTTTTAATTGGCTGGG CATGGTGGTGCATGCCTGTGGTCCTGGCTGCTTGGCAGTATGAGGT GGAGCCCAGGAGTCAAAGGCTGCATGGAGCCATGATCACGGCACT GTACTCCAGGCTGGGTGACAAAGTGAGACCCTGTCTCAAAGAAAA AATAATAATAATAATAATAATATCCAGGCTGGGGGCGATGACTCAC GCCTGTAATCCTAGCACTTTGGGAGGCCAAGGGGGGTGGATTGCTT GAGGCCAGGAGTTCAAGACCAGCCTGGGCAACATGGTGAAACCTC GTCTCTACTAAAAATACAAAAATTAGCCAGGTGTGTGGGCACACAT CTATAGTCCCAGCTACTGGGGAGGCTGAGGCACAAGAATTGCTTGA GCCCGGGAGGTAGAGGTTGCAGTGAGTGGAGACTGTGCCACTGCA CTCCAGCCTAAAAAAAAGAAAAAAAAATGGAAATACCCCTCAGTA GGAGAGAACATGGTCTACATTCTGCCTTCCGAAATCCATATTAACA TTTGGTGGCTGCTTGTTGAAGCTAGGTGATAGCATTAGAGAGTCCT GGTGTCATGAAAGCCAGAGCATCCTAGTGAACTTTCAGGGATGGG GTGGAAGGTGGAGAAGAAATGGGCTATGGAGTAGTTCAGAATGTC TCCAATGGGGCTACTTTTGAGAGAGAATGCTCTCTTTCACCATTTGT CTTCCAGGATATGAACAGAATATAGAGTTGCTATCTTCCTTAGAGT GTGAAAGTCTAGGCTGTCTGCAAGACAGCATGTTATGGTTTTTATT ATTTTTTATTGATTGATTGATTGTAGAGACGGCATCTCGCTGTGTTG CCCAGGCTGGTCTCAAACTCGTGGCCTCAACTGATCTTCCCACCTC AGCCTCCCAGAGTGCTGGGATTATGGGTGTGAACCACAGCACTTGG CCATGGTAATGGTTTTTAAAAAAGGGATCACCAGCTGTGAACTTGG AAGCCTTAGGTGTGAACTCTGTGATATTATTCAACCTCTCTGAACCT ATTTCTTACCATCAAAATGAAAGTTATCTGCCCTATTTAGCTGATTG GGTTGCTGTGTGGCTCAAATGATGCAGTCAATTTGTAAACTGTAAC GTGCTGCACAGATGTTAGGTATTCTGGTCTTCTGATTGTGTGCTTGG CTTTCTAGCTGCTTGAAGCCGCTCAGAGCTTATGTATCACCAAGGG TTAGAGATGTAGTGCTACCCACCTCTTTCATCCTGCACCCCCAATTT CTCCACTTGTCCATTTCCACAAATGTATCCCTGGAGACACTGTGATA ATTTC 187 NON_CODING GAAACTCAAGGCATTTATCTCTTTGGGCTGCTTGTCCTTGCCTGAGC (INTRONIC) TGAAGCCTGATGCCTCCCATAAGTTG 188 NON_CODING TCCATTTCTTCGTTCCACATGACCACAGTTTGCAAGTGTATTCCATG (INTRONIC) GAGAAGTGGAGTGATTGGGAATTAC 189 NON_CODING GGTCCAGGAGTAAATGCCAATTTCACATATAATGTAGACAGATTAT (INTRONIC) CTGATGGGCATCTATCAGATACAAAGTCTGCCCCTTTTTCATGTCCT TTTTGTCTAAATATAGTCATTATCATCATCATCATCATCATCAAATC ATTTCATCACCATCAGAAATGCTTATACATTATCCTGATGTATACCA AAGCTACTGTTTGGAAAGAAACTAAAATAAAAGTCCAGGTCACTTA ACCATACAGGGCTGATGTTAGATGAAAGCAAGCATCGATACCAAA TGCAATTTTACATAATATTACCTGTCAACAAAATATATTTGGACAG CCGCATGGTAATTTTACACATTATGTGTAAACAAAGTATTGGTGGC ATCACATGGTAAAAACTCAGTAATTTCACCTCAGAAATTCTTCTTC ACATCAGAAATGTAGTTTGTGCATTGAGGCTATCTGATTGATGTTT ATGCCTCTCTGCTTGGGATATATTCATGAGAATAAATAATAGAAAC CTCTCCCAATGAATGCAGTCTGTCTGAATTCATTGATCTTTATGCAG TGGAGATATTCTGCACAAGCCGCTA 190 NON_CODING CGTACTCTTGCTAGGGCTTTTCATGGAGATGTAGAAATGGTAGTAA (INTERGENIC) GTGCCAAGGCCCCAGAACCCTCATGTTTGGGTCCGACTCCCACATT GCCAGAGACTAGGCAGCTCACACAGGTGTCCCAAGCTGTCTTTCTC ACAGGCCGCATTGAAGGCATTTATGAAATGAGACCCCCTCTTCCTC ATCCGTAGTGACAGGGCTG 191 NON_CODING TGGATAAAACTTCAGCCGGCCTTCTCTTTATGTGCCTGGCGCCTCTC (INTRONIC) TTTTCTCTGGGTTTTTGGAAGTCTGCCTGCCCAGCCCCTCAGCTGGG GCCTTCCCCACTTCTGCCCCGCCCCACTGGGTCCTCCCAGGGTAGG AGGCAATCTCTGACTGTCTTCCGAGGCTCTGTTGCTTCTCCTTCATC ACCAAATGCCAGGAATTTGTCAGATGCTGTTTGTAACTCAAAAGAA AGAAAGAAAAAGAAAAAGATACAGGAAGGAAGGAAGGCAGAAAA AGAGAAAGAAAGAATGCGTGCAGCAGATGTTGGGAAAGTTAATTT CTTCATTATTTTGCATCCATCCCAGTTCGGATCTCAGCATGGGGTAG GGAATCCTCTGTTGTCCCCATCTGTCGAGGCAACAGTGAGTCCCAT CATG 192 NON_CODING CAACCAATTGAGACACTGAGGCCTAAAGAAATTATTGGCTATAATA (INTRONIC) ATGAGGTGATTGCCTTAGCTATCACGCCAGATTTGCTCTTTTGTTTT CTCCTGATATTTTAAACTCTTCCTTGCTGGAATATTAATAACTCAAA GATAAAAAGGGTACAACTTGTTTCCATGTGGGAGGTAGGAAGAAC ATTGCTTTTGGAGTCAGTTCTAGGCCTGGTGACTCTTTGACTTGCCA GTTGTGTGCCATGATCACTCCAAGCATCCATTTTCTCATGTGTAAAA AGCATGTTAAAAATTTTAAATGAGGAGTTTAAAAATTACACTCCCA GTAGGCTTACTATGAGGACTAAAATAAATAAAAGTGTGAAATGCA GTGCCAAGCACATAATAGCTGCTCAATAAATGGAAGCTAAATTATT TTCCACAGTTATCTTTCAAATTTCACTTTGATCAGTTTTCACAGACT ATCTTCTAAGCAAATTCTGTAGGTGTTTGCCTTCGGAAAAGTGCGTT TGTTGTCAGTGAATGGTTACAGGGAAAAGGAGATACTTGTCATGCA GCTGGAAACATGAAAACTTGGCCCTGTGTTCTTAAAAATGAAAACT CCCTGCAGGATGGGTCAAGTTGCTACCATAGGCTGGAGCCTATGAT TCTCAGAGCAGCATCACTCTTAATGGCACTGTTCTGCATGCCCTTAC CTTGCTCATTTTGCTGGGCTCAGTACTAATTTTCATCCCCTAGGCAG GCAAACTAAGTGTCATTGTGGCAGTTCCTTCCATACTAAGAGGAAG CATTGATCACTAAGAGTCAGCATGGTTTACTATGAGTAAATTAAAC CAGACCTATCTTGACCTCTGACAAGGTTGTCGTGATGACCATGTCA GTTTGGTTCCTTGCTGTATGCCCAGTGTCTGA 193 NON_CODING CGCCATGGGGTGGTTCGAAGAACCATGATGAAGGCTGGTTCGAATT (ncTRANSCRIPT) GTGATGACCATTTTTGTCCACATCTCCTAGGACCCATAAGCCAGAG TTTCTCTGGAGCTTATAGCTAGAAGGGGTTCTGGGTCCTGGAGTGC AGGCCTGTCAACTTTACAGGAGAGCACTAGATTGCTTTCTGAAGTG GCTGAACCAGGTTATGCTTCCATCAGCTGTGTATGAGCATCCCCAT CTTCTTGACCACACTTGAAGCCATCAGTTTCCTTGAAGCA 194 NON_CODING TATGTGCAGCACAAAATGTCGTTTCTTATGTTTGTTCCTATAATGCG (INTRONIC) TTCTGGCACTTATGTGATGCTTCACTTAAAAATACTTAGCTCTTTCT TTTTCCCCCCAAATCAATAACTTTAATGCCTGCTCCAAATAAGCTAA AATAGTTTTGATAATTTTCTAGCAAATGGCAAACTTTTACCTTTTAG CAGTTAAAAACTTTCTGAAATATTTAAAAATCACTTTGACAGTATA TTAAAGTGAGTGAAAGTCTTTATCTAAAGATCCCACTCAACTTTTC GTGTACTTAAAATATTATAGGAAAATTGAGGAGGTGACTTATTATA GAAATAAGAAGACTTAAATGAATAAATTTTCTGAAAGGAAAGTGA CTCTTGTGAAAGATCTCAAATGGCAGACTTCATTTTGTGTTTTATCT TTGCTGGCTTTTACTCACCTACACTCATTTACAAATCCATGAAAATG GTTCAAAGGTCATTGGTGAAACTTGAGAACAAATGCAAAACTTCCA ACTATGGGAAATAGGTAGAAATACATTTTAAAAACATTGGGTTTAT TAAATTGGGTTGATTTTATTACTAATTTATAAATCAGTCAAAAATGT AACGCCAAGTTCATTGTCCTAGAGCGAA 195 NON_CODING GCACTGCCGTACTCTTGGGAAATTTGTCCAAGGCCACCCGGCTGAG (INTERGENIC) CAGCGGTTGAACCAGGACACCATCAGGCATGCGTTTCTTGTCTCCA CCACACCCTCAACCCACTTCCCAACGCGCCTTGCGACAGGGGCTGC GGTATTGCATCCACATGACTGATAAACTAGTAAACACACATGAATT CATTTTAAAAGTGTATTCAATCAGTTAGGTAAACTAAAAACCTTAA GTCTTCGTTCGATTTGGAATGCAGCCAGAGAACAAATGGAAAATTT TTCAAGGTAGAGAAGATGAAAACTCAGAACGCCCTCTTGTGGCATC TCTACCCACCCTAGGAACACTATGGCTCTTCCCCTACACATGGTGA TTGCTAACCTTGCTACAAGACGTTGGACACACACACACACACACAC ACACACACACACACACTGAGGTTCCTTTTGCCCCCTCACTTTTGAGC CAGTGACTACTGAAACCCTCTCCATTGTTGCACCACCAGCAATGCC CCCATCACTTCCTCTCATTTACTTCCACAGGCTGGTTCATCCTCAAA GCCCTCCTTACGTAGATCTGTG 196 NON_CODING TCTGGCAGCTCTTAGTCATGTCTTGGAGGGAGGACGGGCATCCAGG (INTRONIC) GCTGACCGGTCAACGTCCAGCACCTCCCAGGGACTATGGGAAGACT GAGTGGTGGGTCTCGTCCTCTCGGGATACTTGCGCTT 197 NON_CODING CCATCCAGCTGATCGGCTCTAGTTCTATGGTCCTGTTGGCTTCTAGG (ncTRANSCRIPT) ATTCCTTGTTGTTGTAGTCAATTGGGGGAAGAAGGTGCAGAGGGAG TGCACAGAGTTAACATCCTATCAGCCCAAGCTTCACCTCGGCACCC GAGTCTCAGGCAGTCTCCCTGGCTTCTACATAGGCAGTGCTTCTTCC TCATTGTGTGGGGCTTTGATTTTGTAATTCCAAGAGCCTGGGGCTCC TGGCAAGGAAAATGGTTTTCAAATAATGGTTTCGAGAAACAAAGCT GGGGAAGAGGCAATGTAAGCTCAGGCTCTGGCAGGCAGGCAGAGA TCCTGGGAAGGCTGGGTGCTGACTGCACATGGAGCAATGGGAAGG GATGCTGGTGAGAGGAGACGGGGGCACTTAAGCTCCGGCCCCAGC TCTGCTCTCAGTGCCCGGCTCTGTGGTCTTGGGCTGGCCCCCTCCCT TCTCTGGGCCATAGTTTTCCCATCTGTATAGCAAGGCCATTGGACA AAATGGTCCCTCTGCAGATGTGGCTTCTGAGTTGTTTGTGCCTGAG GGACAGCCAGTGTTGGGAAGTTCCCCCAGGAGGTCCCTGAGCCGA GTCTGAACTTTG 198 NON_CODING TGTTCTGAGTCAGGCATGGAGGTATCTTCTCATAATCAAAAGATAA (INTRONIC) GCAAGAAACAGTTAACTGCCCGCAAGGATTCCACAATTTTGAATCC TAACTTCAGATGCTATCTCCTTACCTCATTTGGCACGTGCATTTGTG CTGGTATACATACCTTTTTCAGCACATAAACTCATTTGGCACATGTG CCAAGGATTGCCAACTATCTTA 199 NON_CODING GTCACCATGGAACGTGTGCATAGATGATGTTCCCGTGTCTTTCA (INTRONIC) 200 NON_CODING CAGTTCTCAGACATTTACGGGAAAGCTCTGGTGGCGTGTTAGATGC (INTRONIC) AGTTCATCTCTCTCTGTTTGCAGCGCTCTCAATAGAGACC 201 NON_CODING CTTGACTGTCACGATAGAAAGAGGAAGCAGAAGAATGAAGACAAA (INTRONIC) GCCATTTAAAATTTTCTTGTTCTTTACCTTTTGCATAAAAGGTATTC AGTTCACAAATGATGTAAAATTTAATTAAGGCAAGTGACTGTCCTG AGAAAGTCATTAAAACCCTCATGTCATTTCTCTAATCAAAAGGCTG CCACGCTTCTATTATTTCTTTATTACAACCCTTTATTTTTATTTCTTC AAGTTAAACTGGAGCCTGAGCCATCATAAGCCTCTTGCTAGTGATT TTTTAAATCAGTGATTTACACTTTGAAAAACCAATTTTTTTTATTTTT CCAATTTATATTGGTTAGATCCATAGGGTCACTTTGA 202 NON_CODING GGCTGATGACTTCTCACAGTGTATCTCAAAGCATTATTGCATGTCCC (INTRONIC) ACTTGGTTGATAGGGCATCTCTAGCCTGACAGATTTATCTGTTGAG AACAGGATTATGCATTTGAAACCAGTTTAATTCTTAGCAAGACAAT GCACATGTCTTATGTAGATTTTGTTGTTGGTTTTTTTCTCCTTCGTAA GTTACTCGGGGAAAGTCATGTCAATATAAATCAGTGGTAATGAAAT CAACATTATAGCATCTTTGATAATGCATTTGCTAAAGCCTTTCTGGA CGTTTACCCAGCTCTCAATGA 203 NON_CODING CAATTTCCACCGCGGCCATTTGTTAAACGCATAGCTGCCATCTTCA (INTRONIC) GTGATTATTTCCAAGTAACATCTATGTTTCTGAATAAAAATCCATTT GAATCTCAAGTCAGATTTGCCAG 204 NON_CODING ACTCGGTGAGCTTAACCGTACACTGAGCTGGTGCAGCCGGGGATCC (INTRONIC) ATCTCAGCCCCTGCTTCCCACTCAGCCAGACCCAGACCCTGCATTC CAGCTTTGGTTGTGTGGATTCTCTAGAGAAGGACCCTTGGCTGTTTG TCCCCATGCATTTCTTGATGTCAGGCAGCAGCATCTGCCAGTTGTG ACTGTCCTGCCTGGACTACAGGTTTGGTTGGGTGTGCCCTACAAAC CTTGCTCCTCTCAAACGTGCTCTGCCGTGGTGTAGCTTCTGGCGCTT CACTCTTCTGTCCGCTGGGATCCCTAGGGGGGCTGGATGCTCGTAC CAGACTGTGGA 205 NON_CODING GTTTGGCGTAATACGGAAGCCCTCAGAGCAGTACGCTTCAAGCAGT (INTRONIC) TTATGAAGTCCTTAGCGTCTTTCTTATGGCCGAAAATAGTTTGGAAT GGGTTGAAACAATGGGCCAACCTAACCAGATGAAACTG 206 NON_CODING ATAAATAAGTGAAGAGCTAGTCCGCTGTGAGTCTCCTCAGTGACAC (ncTRANSCRIPT) AGGGCTGGATCACCATCGACGGCACTTTCTGAGTACTCAGTGCAGC AAAGAA 207 NON_CODING TCTATGCGGCCACCCAGATTTCTTGGGATCTGATGCTAGACCTTGG (INTRONIC) AGG 208 NON_CODING CCATATGAAGTAAGGACTGATTATCCTTTTTTTATAAATGAGGAAA (INTRONIC) TTGAGTCACAGGGGGGTTGGTAGCTAGTCTAGGATCACACAGTTTG TTGGAGGGGGTAGTGTATGCACGTGCCCACTTTTTCA 209 NON_CODING GGCCCTGCTGCCTAAACTGTGCGTTCATAACCAAATCATTTCATATT (ncTRANSCRIPT) TCTAACCCTCAAAACAAAGCTGTTGTAATATCTGATCTCTACGGTTC CTTCTGGGCCCAACATTCTCCATATATCCAGCCACACTCATTTTTAA TATTTAGTTCCCAGATCTGTACTGTGACCTTTCTACACTGTAGAATA ACATTACTCATTTTGTTCAAAGACCCTTCGTGTTGCTGCCTAATATG TAGCTGACTGTTTTTCCTAAGGAGTGTTCTGGCCCAGGGGATCTGT GAACAGGCTGGGAAGCATCTCAAGATCTTTCCAGGGTTATACTTAC TAGCACACAGCATGATCATTACGGAGTGAATTATCTAATCAACATC ATCCTCAGTGTCTTTGCCCATACTGAAATTCATTTCCCACTTTTGTG CCCATTCTCAAGACCTCAAAATGTCATTCCATTAATATCACAGGAT TAACTTTTTTTTTTAACCTGGAAGAATTCAATGTTACATGCAGCTAT GGGAATTTAATTACATATTTTGTTTTCCAGTGCAAAGATGACTAAG TCCTTTATCCCTCCCCTTTGTTTGATTTTTTTTCCAGTATAAAGTTAA AATGCTTAGCCTTGTACTGAGGCTGTATACAGCCACAGCCTCTCCC CATCCCTCCAGCCTTATCTGTCATCACCATCAACCCCTCCCATGCAC CTAAACAAAATCTAACTTGTAATTCCTTGAACATGTCAGGCATACA TTATTCCTTCTGCCTGAGAAGCTCTTCCTTGTCTCTTAAATCTAGAA TGATGTAAAGTTTTGAATAAGTTGACTATCTTACTTCATGCAAAGA AGGGACACATATGAGATTCATCATCACATGAGACAGCAAATACTA AAAGTGTAATTTGATTATAAGAGTTTAGATAAATATATGAAATGCA AGAGCCACAGAGGGAATGTTTATGGGGCACGTTTGTAAGCCTGGG ATGTGAAGCAAAGGCAGGGAACCTCATAGTATCTTATATAATATAC TTCATTTCTCTATCTCTATCACAATATCCAACAAGCTTTTCACAGAA TTCATGCAGTGCAAATCCCCAAAGGTAACCTTTATCCATTTCATGGT GAGTGCGCTTTAGAATTTTGGCAAATCATACTGGTCACTTATCTCA ACTTTGAGATGTGTTTGTCCTTGTAGTTAATTGAAAGAAATAGGGC ACTCTTGTGAGCCACTTTAGGGTTCACTCCTGGCAATAAAGAATTT ACAAAGAGCTACTCAGGACCAGTTGTTAAGAGCTCTGTGTGTGTGT GTGTGTGTGTGAGTGTACATGCCAAAGTGTGCCTCTCTCTCTTTGAC CCATTATTTCAGACTTAAAAACAAGCATGTTTTCAAATGGCACTAT GAGCTGCCAATGATGTATCACCACCATATCTCATTATTCTCCAGTA AATGTGATAATAATGTCATCTGTTAACATAAAAAAAGTTTGACTTC ACAAAAGCAGCTGGAAATGGACAACCACAATATGCATAAATCTAA CTCCTACCATCAGCTACACACTGCTTGACATATATTGTTAGAAGCA CCTCGCATTTGTGGGTTCTCTTAAGCAAAATACTTGCATTAGGTCTC AGCTGGGGCTGTGCATCAGGCGGTTTGAGAAATATTCAATTCTCAG CAGAAGCCAGAATTTGAATTCCCTCATCTTTTAGGAATCATTTACC AGGTTTGGAGAGGATTCAGACAGCTCAGGTGCTTTCACTAATGTCT CTGAACTTCTGTCCCTCTTTGTGTTCATGGATAGTCCAATAAATAAT GTTATCTTTGAACTGATGCTCATAGGAGAGAATATAAGAACTCTGA GTGATATCAACATTAGGGATTCAAAGAAATATTAGATTTAAGCTCA CACTGGTCAAAAGGAACCAAGATACAAAGAACTCTGAGCTGTCAT CGTCCCCATCTCTGTGAGCCACAACCAACAGCAGGACCCAACGCAT GTCTGAGATCCTTAAATCAAGGAAACCAGTGTCATGAGTTGAATTC TCCTATTATGGATGCTAGCTTCTGGCCATCTCTGGCTCTCCTCTTGA CACATATTA 210 NON_CODING GTGTCCCTGTTGTGGTACTTCTGCAAGTCCTCCTTCTGGATGGCCAC (CDS_ANTISENSE) CTTCCCTGCAACACAAGCAGAGAAGACTTCACCACGGGCACAG 211 NON_CODING GACCCTCGTAGTGTGCCGGTCAATGCTTGCCTTT (INTRONIC) 212 NON_CODING TGCAGGGCGGTTTGCCGCTGCCACCCTCGGCACCATCTCTGAACTG (INTRONIC) CCCGCTTTTCCGGAGGAGCGGAA 213 NON_CODING GGGTGACGTTGCTGATAGCTCAATACTTAACGTACAGCAGGAAGG (INTRONIC) AGCACTGAGGCAGTGGCTTGAGCTCAGTCTGTGGGAGGAGACCTGT TTTGATCCAG 214 NON_CODING CAGGGTCTGATGATTTTGGCGTTTCCCTGCTTCCCAATTGACCTGGC (INTRONIC) TGTGCTGTTGGCTGTTCTTGCACACTCAAGGTGGTTTTGCCATTGGC TTCCTCCCTCAGCCTGCCTCTGGGATTATGCCACTGCTATTCTTTTTT ATCTACCATCAGCACAATGAAATCATCATTTTTGTCTTCAAGGTACC AAATTCTGGTGATATTGGTGCTTTCTTGCAGCTACTTATCATGAGAA GTGAATGGTCTCATAGTGAACACAGTCATGGTTATAGTGTTCATAC GTTCCAGAGACATGTTTCCTATAATTATGCCCTGCACATTTTTCTAT CATACAATCCTTAGATTACAGCTCTTTGGTTTTCAACAGCTTTGTCC AATTCCATCTTTCCCAGTTTCTCTACCTTGATGAAATATCCTTCTTG CCTGGTTTTACATATTTAAATAACAAATTCCAAAAGTAAAGAGTAT CTGAGGCAGTCACATGACATAAGGACAAATTCAAGCCATCTTGGAC TTGCAGAGGGTGGGGAGACCGTGTCAACACACACAATTTTAAAAA TTTCTTCCCTTTCAATCTTTTAAAAACAAAACTTTTTATAAAATAAA AATGTAATTTAAAAAGGCTACCTGTCTTGGCAAGTAGCTGATCAGC CTGCATTGGTGAGCAGGCCATTCCATAACCTGGTTTCTTGCTCCTTA ATTGACAGCATGGAGCTAACGTACTTAATTTCAGCTCTTTCTACGTG ATTTGACTCATTCTGTTAACATTAACTGTTTTTCAGTCTTCTCAACT AGACTGAACTCCTTAAGTGCAAGAAATACACGCTTAGTAAATGTTT GTTGGACCAGACACTGCACCTTATGAAATTAAAGACCAGAACATTC TCATGGTAGCATTACAGACACTGATGGCAAAGGTACTGTGGGATTT GGGTTTGGCTAATAAGCTCTGTGGTGGTGTTTCAGAAGGAAAATGG TGCTCTCTTAGTTCTATGGAACATAGTGGTCCAGATCTTCTACTGTA ACCAGGCCCAAAGCTGGCTAATCTGGAGGGCTCTGCCTTAGGGATA CTTATA 215 NON_CODING ATTCTGAGTTACCAACACGTTGTGCGTGCATTGATGACCCGGCTTC (INTRONIC) CTGGCCTGCCCTTGGTGCCTGAGCCCCAGTAATGATTGCCCTCTATG TTGGGAGAAGAAGGGAGAAAGTAGTACAAGTAGTGAAGAAAAAA ATGTAGGTGGTGTTGGTGGTTGAGAGTACATGGCACA 216 NON_CODING GTAAGTGAGTGGGCCTGAGTTGAGAAGATCCTGGCCTTGGA (ncTRANSCRIPT) 217 NON_CODING ACCTGCCACCGGCTGGCACACACCACCC (INTRONIC) 218 NON_CODING CTGCAGCCGAGGGAGACCAGGAAGAT (ncTRANSCRIPT) 219 NON_CODING CATCCCGAAGTGTGGCTAAGCCGCCCGGAGGAACACAAAGGGCAT (INTRONIC) ACGCGCACGCACACTTAAAGTTTTAAAACACGATTTATTTATTTTTG TCTGCTGCAACGCTGGGAGAAATGTGGTCTTTGGAAGGAAGCTCTC CAGTGTGTAACCTTCCTATTATTTTGGCCCCCACACTGTGGCTTTAG TAGAACAGGAGCAAACAAGTTTATAAGGCAAGGAGGTGGAGAGAT TAAAAGAGCATTCTCTTGCATTTATGAAGTGTCACTCCGGTGTGTAT GTAGGTGAAGCCTTTGGCCTCGTCTGAAATGCCCATTAA 220 NON_CODING TCTGAAGAGCAAGCGCCCACTGATGCTGAGGTCAACAAAATCAGA (INTRONIC) GAAGCTGACATTTCCATTTTTTGCCAATACTTCAGGTGACCTCATAA TGAAACCCTTGCTGCTCTACAGAAAATTGTGCCCAAACCCTCTCAG GGGAAATAAATGAGCCAAGTTTCCAGTGTACTAGCAAGCAAACAG AAAAGCCCAGATGAATCTTCCTCTCCTTAAGGGATGGTTTGAACAG TACTTTCTTGTGGATGTTCAAGACTACTTAAAAGAAAAAAAAATAC CTTGAATTCAAAGTCCTGCTGATTCTTCAGTCTATTTGGTGCTTCAG GTACATTTGCCAATATGCATCCTCATGGTAAGGTTGTCTTTATAACT AGCCACATGTCTGAGATTCTTGAGCCTTTCAGTCAGTGTTTGATCTG GCCATTCAGGAAGGCTTATTATAAACTAATGTATAACTTTGTTCAC AATCTCGCAAAGTTTCCACTGTCTGAAAATCCTAGTGCATGAGACT CCTACATCGTTATTAATGGCATATCCTTAATAAAAGTTTGGCTTTTG ATTTTTAATGGGTTTTCAGGAGATAACTTCCCAAAGAGGCATTAGA TAGTTTAACAGAGCCTGTCATTAATGTGACCTGTGAGAAGACTTGG CTAGAGGTGGTGAAATATCTTTCCTCTATCCCTCCCAAAGACAAGA AAAACCTATGGATGAGGATGAAAATTTGGCACAAGAGCAATCATT GGCGGAAGTTGAATCTGAAACTGTTGACACCAATTCAAGTTAATGC TGCTAGAGGCTGATCCTCAGGAAGCTTTCTTGTCTCCAGAGGTTATT ATCATAAGTGATGATGAAGACAATTAGGAGGCTGTGGGACTGGAA ACAAATACAGCAATAAGAAACAGGAGCAAAATTTTTAGAACAAGA TTAAAACCTCCCTAAGAAGGTAATTAAAATTGGCATCTTTACATGT GTCAGATATTACCTGTTCAAAATTTGAGTGACTTAGAGTTCTATAA AGAGGTGCTATGATGCCATCAAACATAATCATATTGGACAGAAAC AATCTTCAATAGAACTTAAATCATGTGCCATTTAATACTGTTGCTGG ACAGCTGATAAAACTACCTTCTGACAAAGTTTGATTTAATTAGACT CTAATAAAAGGTCCTATGAGACTTTCTAAAAGACTATATTGGGAAG AAAGAAACCTCAGAAAAGTCTAAATTATCAAGTAGTACCATTTAAA TACTCTTACTGGACAGCTAATAAGCTACCTTCAGACAAAGATTGAA TGATTAAATTGAACTCCATACAGAACTGCTAAGGTGTCTTCAAAAA GGACTTGAGAAGATGAAAGCATCTTTAGAAGGGCCACTTAAATTCA CTTGCTTGATAGAAATAAAGCCTCAAGCAAGTTGTTATAACTTCAG GATTCGACTTCACTGACTCTAAGAGTATAGACATCCATAATTTGAA CTAATGAATAGTCCACTTCTGTTCATTGCTTCTCTGTCACCCCCATT TGCCACTACCATAATGAGTGATAGATACATCTTCATCACCTCTGGA AATCATCTCAGGATCTAAATGGAAACTGTATAAAGCCTATCATTTT TACTGATTTAAACTATGTAAACTCATTATTCTTTTTATGTAATGTGC TGTTGTTATTGTTTACCTGCATAAAAATATTTATGAGGGTTTTCAAC AGTTTACTTGAGACCTCATTTTTGCCCATTTTTTTCCTTCCCGATATC ATGATCTCCTCAGCTGAACTTTCTTACCTTGGGGGTTGTTCAGGAAC TGACTCTCATGGGGAAAGAGGGATTACTATTTCTGTGTTCCTATCTC TTGGTAACTGCTTAACCACAGTCAGTCTTGAACTAATGGAAGGAGC ACTGGACTTGGGTTCTTGAGACCTGGGTTCATGTTCAGTTCTGCCAC TGATTATTGTGACATTGGGCCAGTCACTTGATTTCTCTGAGCCTCAG TTTCATCACCTGTTAAGTGAGGATAGTAATACCTGGCACAAATATC ACAATATTAGTGATAATTGAATATAATTATAAGTACCCAATGGCTA TTAAAAGTAAAACTAGGAAGTGCTGAATAACCATAATATCATTATA TTTGTAGCATTTTGGACCTTATCAATGAACAACTGAGAAAACTAGG TTTTTGAATTCTTTTACTTTTTAAAGTAACTTCCTCCCATTTTATGT CAATTATAGAAAATTTTAAAAAGAAAATTAAATGTGCCTATAATTT TATAAGCCGGAGGTAACTAAGTTGGTATTTTTCTTCTTAGTACCTCT TTGTCTCATCATAAATTGTTCATCAATGTCAAAAACTTGGAAAATA AAGATAAGCATATAGAAAAAAATAAAAACCACCCATAATCACAAA TCCCAGAAGCAATGTTAATATTTTGGTGGATTTATTTCCAGTCTTTT TCTATGGCTATATGTGCACATATATAATTTTTACATAGAAAAAGTC ATAATGCATACAGCTTTGTTGCTTTTAGCATTTTTATCATGAATATT TTCCTACATTTATGCAAAGTATTTGTAAATATCATTTTCAATGGTGT ATAATATTTCATCATAGGATGACATCATGGTTTAGTTAACCATTTTC TTTTGTTGGATATTTGAGGGTCTTTCCAAATTTGGCCATTGTAATTT CACAATGTCTTTTTCATTACCTAACTGAAAATATTTGCTTTGGTGAA AGCAGAGGATTTTTTGTTGTTTGTTTGTTTGTTTTTGAAGAAGTCCT TTTAATAGCTACATTTCATTGACTAAGTGGAACTTCAAGAGACAGG TAGAAGAAAAAAAAAAAGAAACAGTAGATGTAATTTCAAGATTGA GGATTTATTTTGTTAGTGACTGTTCCAGAAGCTGAATTTTGGTGTTA GAGCAATTCAGGAGGGACAGTTTGCCACCATTTTATGATACTTTAC TGTAGAAAAGTTTTCAGGATTTAGACCAGGAAAGAGACATCCTAAC CATATGGGTTGATTTTATTTTATGGACCCTGTGAAGTCTGGGACTGA TCAGGTTTCTCTTTTGTTGGCTACTAGAAAGCTTGGAGTCAAATGTG TGGTCAATGCATAGCACTTGTAATGGGACTCTACGGTATGTATGCA CTTTGTATTAGCTTTCTGCCAGGCTCCATTTCGTGTTCCTATCTTTAT TGTTTTTGTTTTTTCCTTTTACTTTCTTATCTACTTTGAATTTATGCTA TCATGTTGTATTTTGTGTATTCTTGTAAGCCACCTGACATCCATCTT GGAACATGGTGGGGAATAAACACACTAATAAATAAATACATTAAT AAATACATGAATAAATAAACCAATAAGGAAAAAACAATGAGGCAA ATGAATGCAGCCAGGACTCTGAAAATTGCATAGTGCCTCCAAGAAT AATCAATGTTAAGGACTTGAAGCTTGGAAGAACATATTGGAAAGA AGCAGGTGAGGCTGCGAGGCTGCATTTAGAGGTGACGTGTTCTGTG TGACGTCTGTGTCTACTGAAGCATGC 221 NON_CODING GAGCTGGAAGTAGACACCATGTATCTTTTCATTAGAGAAGCAAACC (INTRONIC) CCCAAAGGAGAAGCATTGTCAGGCTTCTCTCTTTGCCATGGCCTTT GCCTATACCCTTGAGCAGTGATCTGAGTCGGCTGAGATGCAGATGT TAAGCCTGGGCAGAAAAGCGCTGCTCTCTGCATGGTCCGGGAGAG ACCCCTCTCCAGCCGGTGGCATGCTCGTTACGCAACACTG 222 NON_CODING GCACCATATGTGAGTATTCCAGATATCCAAGGTCCTCTGGACACCC (INTRONIC) CAGTCTCTTCCACAAAGCTGCCTCCTCAGAGCCTGCTGTCCCGTCTT CTAGGAATGTACCCATTTGAAAACCCACACTCACACTACCACAACA CATACACTGTTTCTTGCTGGTCGTTCCTTTAATCTCAGTGGAAGATA TCTCATAGAGAACTGTTGGTGATTGCTTAACTTGGTTGGGAGGAAA ATAGATCAAGCAGGTGACAACCTGCATATTGGGGATTTTCCTATGC TGAAAATTGTTATTCTGTTGCAGCACTCCACCCTCCCTTCACAGCCC CAAAAAAGAGAAGTACGAGTGCTGCTGATGTTCAGGGTTTGAATAT GTTTTGGTTTAAGATGTTCAGTGGAATTAGAGAGAATTTCATCCTG GGCAGTGCAGTCAGGCTGGAGGAGTATTTTGGTTTCATATTACTAA ACCTTGTTTTCCCATCCCAGCTGCTTGTGTGCTATCTTGGGGCCACT GAGAACCTGGCTGGGCTCTGCGGGGTGGGAGTGTTGTCCCGGGGCT GAGTCCAGCCAGGGGTGAGGTCGTCTTGGTGCACATCTTGCACGTT GCATGAAGCTCAGAGCC 223 NON_CODING CCCAGACCCATGTGCGGCTGTGCAAATTCTTTCTGGGTTGA (INTRONIC_ ANTISENSE) 224 NON_CODING GCAGCGCTGGATGCCGGAGCAGGTGCTTCTGCAAGAAGCTGTTCTG (UTR_ANTISENSE) CATCCTCTCCTTGCTGCATCTTGGTCCACTGCCTC 225 NON_CODING TCCAGGCCAGCCAGGTATTGATTGAAGAAATCTAGAAAGGCAAAT (INTRONIC_ GGACCACTGTTATACTGACAGTGTTTGTCTAACCAGCTGAGTGTGG ANTISENSE) GCATTTTGAGGAATGGGGCCAGAGAGCCAAGCCCAGGGCTACTGC AAGTTGGGAAGTCTAATAGATTCTACTTCTACCAGAATTCTGGGAT TCCAAAGAATGATACCTTCAGTGTAAGGGTAAATTAGAAATAAGCC TCCATAGTACTCATAATGGGCCACAAGAAAAACTGACCATTTCAAA TTTTGGCAAGAGTGGAGAAGAGAGAAATTGCCACTGAGAATTTGG AACCATGAGGCAGCCTCACACAAGTTTGTGG 226 NON_CODING CAACCTAGCCCTCCATGAGGACTGAGCGCATGAGAGATCCTGAGCC (ncTRANSCRIPT) ACAGCCGCCCAGCCCTGCTCCTCTCGAATTTCTGACCTACAGGAAC TGCAAGAAGTAATGAAAGACTGCTGTTTAAAGCCACTGCATTTTGG CATGATTTGTTATGCAGTCGTAGATAACCAGAAAACA 227 NON_CODING GGTTTCAGCACCCAAGACTTAGACCCACAAGAACTTAAAATGAGG (CDS_ANTISENSE) AAAAAGAAAAAGTTCAGGTTTAAAGGCCTGTCAGCACTCAGAAAG ATACCTGTTTCAGCTAAACATTTTCTAACTTATTAAGAGAATCTACT AATGTCTACTCTACCTGACTAACCTACAAACACTTCTCACAACTTCT TTTAGGATTGTGACACCAACTGCCC 228 NON_CODING CTTTCTGGATGCACCATTTACCCTTT (INTRONIC) 229 NON_CODING AACATGGGTTTTGTCGTGCTTCTCCTTTTGGCCTCCTGCAATATTCC (CDS_ANTISENSE) TGTTCTTTTTGCTGGCACTGAGATCCTCTCATCTCGGGAAGCTATTC GCTCAGACGAATCGTAAAAGGCTGGCTGGGACCACGGGGCAGGCT GGGGCCATGGAGGGGGCTGTGCTGGGCCAGCAATCGGACTTGAAA CCCCTCTGGAGAAGGCGTCAGGGGGAGGAGTGACTGCAGAGTAAG GTGGAGGTGCAGGAAAGTCAGCAATGGGACTCGTCATGTTTCGGGT TGGCGAGAAGGGGGTAGCTGGCTGATTCACAGACCCTGGGAAGGG TTTGGCCGTTCTATTCATGGGGACCATCCTCTGGATGTTTGCTGTCT CAGATGTCCCACTGAAGCCATTCTGTTGGGGAACATGGCCAAGACC ATGACTCACCTCGATGTAGCTTTTGCTCA 230 NON_CODING CCACCATCACCTGGACGCTGAATGGAAAGACCCTCAAGACCACCA (ncTRANSCRIPT) AGTTCATCGTCCTCTCCCAGGAA 231 NON_CODING TCAAGAAGTCGGAATTTTTAGGACAGTTACAGTCTGCATTTAAGGA (INTRONIC) TCCTGATGGACAGGCTG 232 NON_CODING GAGAGCGCAGTCTTTCTGTCTCATGATACTGATTACCACACAAAAG (INTRONIC) CATTGGTGAAGAAACAACTGACTGAGTTGAGTTAGGGAGTTTTTTC AGAGTAATTTTGACTAGTTGCAATTTTCGATTTG 233 NON_CODING CCGGGACTTGGCAGTACTTGAAACAGGAGGAATACACCAGCCTAA (INTRONIC) ATGTACAGACTTTGTAGCCGAGCCCACTCGATCGGTCTGTGCCTTC ACGTGACCACCATCTGTGCCTCCCTCGCTCCATCCAAATTTGTGTAG GCTGCTCCTTGGAGCTATGCCTAAAATATAGCTACACCAGAGCCCT GGAAACTGTAGTCAAGTAACAGGCCTCACTGTTTTTTTTCTTTGGAT TAAAAGTGTATATCTCTCTACTGAGGGGTTTCCAGCTTTA 234 NON_CODING ACCCTAATGTTTGCCACAATGTTTGTAT (INTRONIC) 235 NON_CODING TTCCTTCTACTCAATCTGACCGAGGTCCTCCAGGTCAAGGACAGCG (ncTRANSCRIPT) AGGCTCTCAGTCCCACTTCCCCTTGGCACATAGAAGAGGCAGTGCG C 236 NON_CODING TTGGAGCCCGTAGGAATATTGAAGAAGTTAGTGAAGAAATGCTAT (INTRONIC) ACAGTCATTTGTTGATTAATGAAGGGGGATAAGGTCTGAGACATGT GTCGTTAGGTGATTTATTCATTGTGCAAACACCATAGAGTGTATGG TACTTACACAAACCTAGAGGGTATAGCCTACTAAACACCTAGGCTA CAAACTTGTACAGTGTGTTACTGTACTGAATACTGTCAACAATTGT AACACAAATCACCAGGCGATAGGAATTTTTTAGTTCTATTGTAATC TTATGAGGCTACTCTCATATATGCAGCCCCTCATTGACCAAAACAT CATTATGCAGTGCATGACCATATTGAGAGTATTCGTTTTTTATTTAC TAAAAAATAGTCAAAACTTGAGGAGGAAGAGACAGATGTCACTAG AAAAAGGGAGAAGTCCGGTAAGGGAGAAGTCAGCTTCCTGAGGTG GAATCGTATTACCTTTGGGATTAGGACATTTCATTG 237 NON_CODING CCCACAGGCAGCTTTGGTGTTCTCATGTTATAGTTCTTAATCTAAAT (INTRONIC) TGTAGGTGCTAAACAAAACTACCTGCCTTAATGGTAGGCAGAGGTA TTTGAAAAATTAATGATCTACTTGTTTGCTGAATGTCCACAATACA AGCTTTGATTTAAAAAAATCATGTTAGGATAGCATGTTTATTACAT ACTATTTATTATCATACTTAATATTTCTTGCCTATCAAAAGTAAAAA CCTGATGCTTTATGTTAAATGTTTCTTGCCCATTGGAGCCTGTTCAT GGCAATTCTTTGTCCAAGAAGAGTAATGGTATTGTCTCTTTCTATGT GTCTCGGTAATTCAGGC 238 NON_CODING TCTAACCTTGGCTCCGGGGTATTGCCGAAACCAGTCCAGGCACGTC (INTERGENIC) ACAAATGTCTGACTTCTCCCAGAGGCTTCAGAAGCACAATGAGCAG CAGAGGAGAGCCATGGAGCCAAGCACAGTCTCATTTAACCTCCCCA AAAGCTTGGGAAGTGGGTGGTGTTATAGCCCCATTTTACAGATGAG AAAAACTGAGGCTTATTTAAGCAGCTCACCTAAAGTCACATATTGA TTGTGCTGAGCTGAGATTGTACCCTAATCTGCCTTCAAATCCATGTT TTTACCCATTGCATGTGATTATGGAACCTGGGACCGAGGAGCAGGA GGAGAACATTCTAAATTCTGCTCCCATCTTGTCTTTACATCTCAGGT CACTTTTAGCAAAGACAGACCCGGACACTTGCCATTAATACTACAG GCTTCCTTCCTCCTACCCCCTTCCCCCAATCTTATTCATCTCACCTCT CCAGTAGGTCGTGGACTCATGCATT 239 NON_CODING GGCAGGGGTTGGGACAAGTGCTAAGTATGCAAGACTCAAGGGAAG (ncTRANSCRIPT) AGCT 240 NON_CODING CCTGGGATGACCACAATTCCTTCCAATTTCTGCGGCTCCATCCTAAG (INTERGENIC) CCAAATAAATTATACTTTAACAAACTATTCAACTGATTTACAACAC ACATGATGACTGAGGCATTCGGGAACCCCTTCATCCAAAAGAATAA ACTTTTAAATGGATATAAATGATTTTTAACTCGTTCCAATATGCCTT ATAAACCACTTAACCTGATTCTGTGACAGTTGCATGATTTAACCCA ATGGGACAAGTTACAGTGTTCAATTCAATACTATAGGCTGTAGAGT GAAAGTCAAATCACCATATACAGGTGCTTTAAATTTAATAACAAGT TGTGAAATATAATAGAGATTGAAATGTTGGTTGTATGTGGTAAATG TAAGAGTAATACAGTCTCTTGTACTTTCCTCACTGTTTTGGGTACTG CATATTATTGAATGGCCCCTATCATTCATGACATCTTGAGTTTTCTT GAAAAGACAATAGAGTGTAACAAATATTTTGTCAGAAATCCCATTA TCAAATCATGAGTTGAAAGATTTTGACTATTGAAAACCAAATTCTA GAACTTACTATCAGTATTCTTATTTTCAAAGGAAATAATTTTCTAAA TATTTGATTTTCAGAATCAGTTTTTTAATAGTAAAGTTAACATACCA TATAGATTTTTTTTTACTTTTATATTCTACTCTGAAGTTATTTTATGC TTTTCTTATCAATTTCAAATCTCAAAAATCACAGCTCTTATCTAGAG TATCATAATATTGCTATATTTGTTCATATGTGGAGTGACAAATTTTG AAAAGTAGAGTGCTTCCTTTTTTATTGAGATGTGACAGTCTTTACAT GGTTAGGAATAAGTGACAGTTAAGTGAATATCACAATTACTAGTAT GTTGGTTTTTCTGCTTCATTCCTAAGTATTACGTTTCTTTATTGCAGA TGTCAGATCAAAAAGTCACCTGTAGGTTGAAAAAGCTACCGTATTC CATTTTGTAAAAATAACAATAATAATAATAATAATAATTAGTTTTA AGCTCATTTCCCACTTCAATGCAATACTGAAAACTGGCTAAAAATA CCAAATCAATATACTGCTAATGGTACTTTGAAGAGTATGCAAAACT GGAAGGCCAGGAGGAGGCAAATAATATGTCTTTCCGATGGTGTCTC 241 CODING GGCGGCCACCAAGTCGCTGAAGCAGAAAGACAAGAAGCTGAAGGA AATCTTGCTGCAGGTGGAGGACGAGCGCAAGATGGCCGAGCAGTA CAAG 242 CODING TCCATTATTGCTGCCCGGAAGCAGAGTGTGGAGGAAATTGTCCGAG ATCACTGGGCCAAATTTGGCCGCCACTACTATTGCAG 243 CODING TGGTGAACAGCCTGTACCCTGATGGCTCCAAGCCGGTGAAGGTGCC CGAGAACCCA 244 CODING AGGAGACCACCGCGCTCGTGTGTGACAATGGCTCTGGCCTGTGCAA GGCAGGCTTCGCAGGAGATGATGCCCCCCGGGCTGTCTTCCCCTCC ATTGTGGGCCGCCCTCGCCA 245 CODING GCGAAGACGAAAGGAAACAAGGTGAACGTGGGAGTGAAGTACGC AGAGAAGCAGGAGCGGAAATTCGAGCCGGGGAAGCTAAGAGAAG GGCGGAACATCATTGGGCTGCA 246 CODING GACCCTGATGGCTTTGGGCAGCTTGGCAGTGACCAAGAATGATGGG CACTACCGTGGAGATCCCAACTGGTTTA 247 CODING ACCCTTCTTCTTGGCGAGACCACGATGATGCAACCTCAACCCACTC AGCAGGCACCCCAGGGCCCTCCAGTGGGGGCCATGCTTCCCAGAG CGGAGACA 248 CODING CACGAACTGTGCGATAACTTCTGCCACCGATACATTAGCTGTTTGA AGGGGAAAATGCCCATCGACCTCGTCATTGATGAAAGAGACGGCA GCTC 249 CODING TCAGACGGGCACATCTATTGGAGGTGATGCCAGAAGAGGCTTCTTG GGCTCGGGATATTCTTCCTCGGCCACTACCCAGCAGGAAAACTCAT ACGGAAAAGCCGTCAGCAGTCAAACCAACGTCAGAACTTTCTCTCC AACCTATGGCCTTTTAAGAAATACTGAGGCTCAAGTGAAAACATTC CCTGACAGACCAAAAGCCGGAGATA 250 CODING CTCTTTCTACAATGAGCTTCGTGTTGCCCCTGAAGAGCATCCCACCC TGCTCACGGAGGCACCCCTGA 251 CODING TGGGAATGTGCTTTGCAGCCGAGTCAGATGTCCAAATGTTCATTGC CTTTCTCCTGTGCATATTCCTCATCTGTGCTG 252 CODING AGCGCAGGAGCATAAGAGGGAATTCACAGAGAGCCAGCTGCAGGA GGGAAAGCATGTCATTGGCCTTCAGATGGGCAGCAACAGAGGGGC CTCCCAGGCCGGCATGACAGGCTACGGACGACCTCGGCAGATCATC AGTTA 253 CODING GGCCTAAGGATCATTTTCTCGGATGCATCACGGCTCATCTTCCGGCT CAGTTCCTCCAGTGGTGTGCGGGCCACCCTCAGACTGTACGCAGAG AGCTACGAGAGGGATC 254 CODING GGGGTGATGGTGGGAATGGGACAAAAAG 255 CODING GTTGGATTGCCAGCTTGTACCTGGCCCTTCTGTTTGGCCACGCTATT GTTCCTCATCATGACCACAAAAAATTCCAACATCTACAAGATGCCC CTCAGTAAAGTTACTTATCCTGAAGAAAACCGCATCTTCTACCTGC AAGCCAAGAAAAGAATGGTGGAAAGCCCTTTGTGA 256 CODING GGCAATGAGCGCTTCCGCTGCCCTGAGACCCTCTTCCAGCCTT 257 CODING TCATCCTCCCTTGAGAAGAGTTACGAGTTGCCTGATGGGCAAGTGA TCACCATCGGAAATGAACGTTTCCGCTGCCCAGAGACC 258 CODING GGTTGGATCCCAAGACGACATATTATATCATGAGGGACCTGGAGGC CCTGGTCACAGACAAATCCTTCATTGGCCAGCAGTTTGCTGTGGGG AGCCATGTCTACAGCGTGGCGAAGACGGATAGTTTTGAATACGTGG ACCCTGTG 259 CODING AAAGCAGAAGCGAGACCTCGGCGAGGAGCTGGAGGCCCTAAAGAC AGAGCTGGAA 260 CODING AGGCCTCCTCACCAGTCAGTGCATCCCCAGTGCCTGTGGGCATTCC CACCTCGCCAAAGCAAGAATCAGCCTCA 261 CODING TTGAGGACATCTACTTTGGACTCTGGGGTTTCAACAGCTCTCTGGCC TGCATTGCAATGGGAGGAATGTTCATGGCGCTCACCTGGCAAACC 262 NON_CODING GTGACTTGGTCCAAAAGACCTGGGCACTTGGTCTAACTTTTCAAAC (INTERGENIC) ATTATCTAACCTCTGAATCTGGAATAACCAAACTGTAAGTTGACTT AATTCACAGAAGTGCAGTGATGGTAAAATGAAATAGCATGAGTAG AGTGATAAGTGTGATGCAAATGAAAGTCATATCTTCATTACTAGGC TTTATTTATTAAATATAGCTAAAGTACTCTAAACGTATATGTCTACA CTTTTTTGAACATGGATAGTTTTTACATAACTGTACTGAAAGAAAG GGCACTAATTACTATGCGCTCTAA 263 NON_CODING AGCTCTCAGGTTCGTGGGAAAGCTAACATACAA (INTERGENIC) 264 NON_CODING ATGAATATGTCAATGCTGAATGCAAATCAGGGAAAG (INTERGENIC) 265 NON_CODING TGAGTGTAGTATTGGTAGGATCCTTCAGCACCCTGCTTCTGTTATGG (INTERGENIC) AAGCTCAATGGGAAAATTCCTCTCTCCCCAGCCCTTGGCAGACAGA GCTCATGATGGTAGAGTTTT 266 NON_CODING AGAATTTTCATGGTGTTATGCATGCTGAAAAATGCATTGCATTTTG (INTERGENIC) AAAATTTTAGCAAAGGATACGTCAATGACTGCAGCATGATTCAGGC ACCTTCCCTGGCAGTCCACAACTCTGTTATC 267 NON_CODING ATGTTCTTGTCATTCGTTAAGTTGCAAAATTCAGCAACTTACAATGA (INTERGENIC) GTATTACTACTATTGTACTG 268 NON_CODING ACTTGAAATTGTGTCCAGAACTGGTGGGTT (ncTRANSCRIPT) 269 NON_CODING AATGGTTGTTCAAGCCAGGCCTGCCTCATTGAAAGGGTGAAATCTT (INTERGENIC) CCTTCACTGGAAGGAAGTGAGAGAATTAGTCAAGCAGCTATCTGA GGAAAGAACATTCCAAGTAAAGAATATACAGCCCATACATTGTTG GATGTGTGTACATTGAAATTTTTGTGCAGTAAAATGAATATTTCATT TACCTATATAATTTTACATAAAATAAAATATATTTTGAATGTGAGTT TGTTCCAAACAAATCATTTTCTTGCCTTCAAAACCACTGAGCTTAAA GAACTCTTTCAAGTGTCATTAGAGATAGATTCCAACTACAATCAAC ATTGTGGAATCCAGAGGAGGCAAAATGAAGGAAGCAGCACTCATT ACAAAATGCTGCTTTGTAAAGAATTAATTCTGTCCTGGTATGTTTCA CATTAGGTAATATGAAGGAAATGAATATGTCATGAACCCTCCTTGA GGATGTGGGGGAATTAAAAGTAATTTCGCTTAATATCCAACTCTCA CTTTTGGCTTTGTAGTCAGAGGGAAACAATGCTTTCCCAGGTTCTA AGGTAAACGTTAAAAGGTTACAAGGAGACTTGGAAGAGTCAAGGA ACGCTTCCACCAACTATTCCTGCCATTCCAGTTGGGAGGGTT 270 NON_CODING AATTTACTGCCTGCTCGTTTGGAGATCTATAACCTTTATACTTAGAC (INTERGENIC) AGTTTTTTAAAAAGTATAACAGCAATTATTTCTCCCAATTTATTTAA TGCCGTTTTTTCATTGCATCCATTAAAATATTTTACTTTTATAAGCA ATGATACCAGGAAGTTATCGTTTGAATAGTCTGCTGGAGGAGTAGG GCAAAGTAGTTAAGATCAATTGTTCTTTCAGAAGGCTGCTGCTTTCT AGCTGCATGACTTTGGGTACGTTATTT 271 NON_CODING CAAACTTTGAGTTTGACCTCTATAAAGACACTAAAA (INTERGENIC) 272 NON_CODING GAACAATATGAAAATACTCTACTGAAAATTGATGAAATTGAAGAG (INTERGENIC) AAAGGCCATTATGAAA 273 NON_CODING ACAGCATTGATAAACCTGTAGCTAGACTAACCAAGAGAGAAGACC (INTERGENIC) CAAATAAAGAAAAACAGAAATAAAAAAGGAGACATTACAGCTGAT AACCACAGAAATACAAAAGATTATCAGGCATTATTATAAACTACA ATACACTAACCAACTGGAA 274 NON_CODING TAATTCAGTATGCTGTCCAGGGGCCTGGAAATCACTCAGCACAGTC (INTERGENIC) TACCACCATTGGCACATGAACACTTCTCCCAGGGTCTAAGGACAGG CTGACATAACATGCTAATACCACCAGAGCTGGCACTCACCCAGATG TACCACATCAGGCCAGGAAGCAGAAACTACCAACATCCCAGCAAA CCATGTGGAGGCCCCCAAATCAGACTGCTTGGGCCTAACA 275 NON_CODING AGGATATCACTGCAGGTCATAAAGACATTAGAAAGATAGTAAGGG (INTERGENIC) ACTACTATAAATAATTTTATGCCAATAAATTTGGAAATTTAGATGA AATTGACAAGTTCTTGAAAAAATAGCACTAAAACAGATATAAGAA CAAGTAGCAAATATGAATAGTTTGAAATCTACTAAAGAAATTGTAT CTGGGGCTCAAGATGCCTGACTAGATGCAACTAGAATGTGCCTCCT CCATGGATAGGAACCAAAATAGC 276 NON_CODING TGGCATGACATAGCTAAAGCACTGAAGGAAAAAGTATTTTATCCTA (INTERGENIC) GAATAGTATATCCAGTGAAAATATCCTTTAAAAATGTGGGAGAAAT AAAGACTTCTCCAGACAAACTAAAATAAGGGATTTCATCAATACCA GATCTGTCCTATAAGAAATGCTGAAAGAAGTTCTTCAGTCTGAAAT AAAAGGATGTTAATGAATTAGAAATCATTTGAAGGTGAAAAACTC ACTAATAATAGGAAGTACACAGAAAGAGAACAAAAAAACACTGCA ATTTTGGTGTGTTAACTACTCATATCTTGAGTAGAAAGATAAAAAA GATGAACCAATCAGAAATAACCACAACTTCTTAAGACATAGACAG TACAATAAAATTTAAATGCAAACAACAAAAAGTTTAAAAGCTGGG GGATGAAGTCAAAGTGTACAGTTTTTATTAGTTTTCTTTCTGAGTGT TTGTTTATGCAGTTAGTGATAAGTTATCATC 277 NON_CODING GTAAACTTAGGAGGCGTAGTGCTCCAGGTTGATCTGGCGGTTGA (UTR_ANTISENSE) 278 NON_CODING GTCAAAGAGATATTCTCCCACGCCAGATTCGGGCGC (UTR_ANTISENSE) 279 NON_CODING TGGAGCGCTCGAGAAGCCTGGGCTCCACTATG (INTERGENIC) 280 NON_CODING GGAATTTCGTAATTAAATGATATGTAAAATTTGAATATTATTTGTTC (INTERGENIC) AGTCTTATTCTTCCAGAACCTCAGTTACTTTCTTTTATTAATTCAGA CAGTTACCACAGTACTAGTCAGCTATTACTCAGTTCTGATC 281 NON_CODING TGGTGTACTAACAGCACTGATTCTGTTAGCAACAAGTAGTGGTAGA (INTERGENIC) CAACTAGAAATATGTCAGTTTAAAACTTGTGAAGTTGGTTGTTACA AATCTCCATTCTGTGTATCTCCATTCTGAATACTAGATACACATCTC CATGTGTATCTCCATTCTGAATACTAGGTACAACGATTTTGTCTCTT GGAAAATTTCCTTGTCCACTGAGTA 282 NON_CODING TCTCACCTGTGGAACTCATTACCTGCATTAAGTTTTCTCTGCTTTCA (INTERGENIC) ATATTCAGTTTAGCCGGGCGCGAT 283 NON_CODING AATATGGCCATGACACCAGAAATCACAAACATGATGAGAATGGAA (INTERGENIC) TGACTGGGGAAGAAGTGCCAGATGCTTCACTTGTAAATGAAGACCC AGCCTCTGGGGATGCAGATACCACCTCCCTGAAGAAGCTGAATATC TGCAGATA 284 NON_CODING CATAGCTAGGCAGTGTTGGAGATCAGCAGGAACTAGACACAATGA (INTERGENIC) ATGGATATGGCATCAATACTCATGAACATGCCATTCTTCCAGCAGT GCTTGGCAACTCAGGTTGAGGAACAGAGAAGGTGGATGGCTTAGG TAATGGAATTGGATGCTTTTTAAATGTCAGTGGCTGTCAAAACTGT ATA 285 NON_CODING ATGTCTCAGACCTCTCCATACTTCATCTGTACTTCTTGATCGCTTTT (INTERGENIC) ATTCTTGAAATTAATACAAGAAGGTCTCTCATTTA 286 NON_CODING CTTAGTGGGGTTTGGAACTGCCTGAGAATATTCCTATAGAAACTGG (INTERGENIC) GTCATCTTGCCTTCTGTGCCACTAGAACCTCCTGTCTCTCCAATAGC TGCTTCTCTCTAATTCTTCACCATAGTTTTCTTTCTGTGGTCTTTTGA GGTTCTCTCCT 287 NON_CODING CTTTCACTGTTATGCCGGTGATTTGAATGTAAAGCAGTTTTATTTAA (INTERGENIC) ATCAATATAATTTAATAAAAACATATTTAAATTTTGGGTTAGATTA AAAATTTTCTCTATTGCCAATACTTGGTTTGAACTCAATTAGGCTCT CTTTACATAAGAGACTACATTAAACACAGACATATATGAGGTATTT TTGAGACATTTGAATGTAATATATTGTAATTTTACCATTTATTTTGT CTCCTAAATTGACATTTAAATAATCAGAATCTCTAGCTCAATATTCA AATTAACATTTTCTTCCCTTAAAATGGTGGGTTACCTCCTTCCTGGA AGGAGCGGAATGTGAGTAACATTTCTTCCTTTCCATGTTTTTCTCAA TCAAATGGCACAAAGGATTTTCTTGACTGCTTGAAAACTAAAAACA GTTTCCCAGAGTTTATTAAGTTCATATTAATTTTTAATGCAAATACC TGTTATTAAAACTCTAAGTAGGGCAGGCGC 288 NON_CODING CATTGGGCTCCAGAGTATCGACGGCGCTCTCCTGTGATGTAGGCCG (INTERGENIC) TGAATTTCACGTGATGTGCACCTTG 289 NON_CODING TGCACCTGTTTAGTTTGTGACAATCTGAGCCCAGTACATGGTTCTCT (INTERGENIC) GATTCCTAAGCCAGGAGTCTCTCTGTAACCAAACTGCTATTATGTG AGCATAGAACAGCTCTCAAAGTAAATGTCCCACTTCTATTTCTGGC AGGTTATGTTTAGCTACCTTTCCAAAAGAGTCCCAATCCTAGTATG CCTTTCAACAGTGTC 290 NON_CODING TGAATAAACTCATTCGTCCCTCAAACCAGAAATTATTTGAGGTTAT (INTERGENIC) CAATAACTTCTCCATGGAAGAGTTTGTTAGAGTTTTGGTCAGGAAA ACA 291 NON_CODING AAGTTCCTGAAGTGTGTCATCCCTCTGCTAGACATCTAAGGGATGA (INTERGENIC) CTTTTTTCACAAATCATATTAACTCACCAGTACAATAGTAGTAATAC TCATTGTAAGTTGCTGAATTTTGCAACTTAACGAATGACAAGAACA TGGCATAGGTCAGTGATGCATGTTATGCTTAATTTTGAGTGAGTGA CTTGCATGTTATATCTCTGCCTG 292 CODING GGTCGCCAGTCATCCCGCACAAAAAACCTGTCCCTGGTGTCCTCGT CCTCCAGAGGCAACACGTCTACCCTCCGTAGGGGCCCAGGGTCCAG GAGGAAGGTGCCTGGGCAGTTTTCCATCACAACAGCCTTGAACACT CTCAACCGGATGGTCCATTCTCCTTCAGGGCGCCATATGGTAGAGA 293 NON_CODING CAGAGAGGTGGTAACTCCCGAGTAAGCAATGCCAATCCTTCAGGC (INTRONIC) AAAGATAAGGAAGAACCGCACAGCTGCTCCAACATAAAGTGG 294 CODING GTATCCTGGCATCCATCTGTGGTGGCCTTGTGATGCTTTTGCCTGAA ACCAAGGGTATTGCCTTGCCAGAGACAGTG 295 NON_CODING ACTAACCTCTGCAGTTTAACCTTGAGCGATACCTTTTCCCATGAATA (INTRONIC) G 296 CODING TGGAGGCTGCCTGATCGAGCTGGCACAGGAGCTCCTGGTCATCATG GTGGGCAAGCAGGTCATCAACAACATGCAGGAGGTCCTCATCC 297 CODING GATCGCCATTCTTGATTATCATAATCAAGTTCGGGGCAAAGTGTTC CCACCGGCAGCAAATATGGAATA 298 NON_CODING AACGATTTCGAGATTTACTACTGCCTCCATCTAGTCAAGACTCCGA (NON_UNIQUE) AATTCTGCCCTTCATTCAATCTAGAAATT 299 NON_CODING TACTGATAATCTCAAGGAGGCAGAGACCCATGCTGAGTTGGCTGAG (NON_UNIQUE) AGATCAGTAGCCAAGCTGGAAAAGACAATTGATGACTTGGAAGAT AAACTGAAATGCACCAAAGAGGAACACCTCTGTACACAAAGGATG CTGGACCAGACTTTGCTTGACCTGAATGAGA 300 CODING AAAATCTTGCAAAATCGGCAGAGGCTTGGGCGGCTACTTGCATTTG GGACCATGGACCTTCTTACTTACTGAGATTTTTGGGCCAAAATCTAT CTGTACGCACTGGAAG 301 CODING GTGGTGAATGTACCTGTCACGATGTTGATCCGACTGGGGACTGGGG AGATATTCATGGGGACACCTGTGAATGTGATGAGAGGGACTGTAG AGCTGTCTATGACCGATATTCTGATGACTTC 302 NON_CODING CAGGAGCTGATCCTCCTTGCAAAGCTGTGCCTTGCAGAGATGCACG (NON_UNIQUE) TGTGCATTTCAGCTACATCATGCCGCGCTGTTGTAATACTGTATAAA GACCTCAATCTATCCAGAGTATTTT 303 NON_CODING TTGCACACTGTTCCAACTTGCCGTGAACACATTTTTTGCTCTTT (INTRONIC) 304 NON_CODING CAAAGAAGCTAAGCACATTGCAGATGAGGCAGATGGGAAGTATGA (NON_UNIQUE) AGAG 305 CODING TGTCTGTGTCAATGCGTGGATGCTGGACCTCACCCAAGCCATCCTG AACCTCGGCTTCCTGACTGGAGCATTCACCTTAGGCTATGCAGCAG ACAG 306 NON_CODING TGGAGTCGTATGATGCCCTTGCCTTGTTTTATATTGGCTGTCAGCGC (INTRONIC) TTAACTGGGACTGAAGTATCTGGGTAACAAAAATTGATATAATGAC TTAATGCGCCTTATTCTCTTTGAGCTACATCAGTTTAGAGCACTTCT GAGAGAAAAATGTCTGGAAAATATCAGGGAGTCATTTATCAACCT GTTTTCATTAGCATACTGCCTAGCTCTGGCAAGGATTTGA 307 NON_CODING CGGAGAAGGTTAGAATGGATTTGAAAGAATGTGGTTGGATTCAAA (INTRONIC) GAAGCCCTAGGAGACCCAACAAGTCAGCATTTTTCTCTTGTGAAAA GAACCACCTGCCAACCCCAGCCTGTTCCATTGCTGACATCAGAGG 308 CODING CTGAAGCTAGACAGGCAGCAGGACAGTGCCGCCCGGGACAGAACA GACATGCACAGGACCTGGCGGGAGACTTTTCTGGATAATCTTCGTG CGGCTGG 309 CODING ATGATAGCAATCTCTGCCGTCAGCAGTGCACTCCTGTTCTCCCTTCT CTGTGAAGCAAGTACCGTCGTCCTACTCAATTCCACTGACTCATCC CCGCCAACCAATAATTTCACTGATATTGAAGCAGCTCTGAAAGCAC AATTAGATTCAGCGGATATCCCCAAAGCCAGGCGGAAGCGCTACA TTTCGCAG 310 CODING AGCAGTCATGCCTGAGGGTTTTATAAAGGCAGGCCAAAGGCCCAG TCTTTCTGGGACCCCTCTTGTTAGTGCCAACCAGGGGGTAACAGGA ATGCCTGTGTCTGCTTTTACTGTTATTCTCTCCAAAGCTTACCCAGC AATAGGAACTCCCATACCATTTGATAAAATTTTGTATAACAGGCAA CAGCATTATGACCCAAGGACTGGAATCTTTACTTGTCAGATACCAG GAATATACTATTTTTCATACCACGTGCATGTGAAAGGGACTCATGT TTGGGTAGGCCTGTATAAGAATGGCACCCCTGTAATGTACACCTAT GATGAATACACCAAAGGCTACCTGGATCAGGCTTCAGGGAGTGCC ATCATCGATCTCACAGAAAATGACCAGGTGTGGCTCCAGCTTCCCA ATGCCGAGTCAAATG 311 CODING ATATCGCTCTATTCTCCAGTTGGTCAAGCCATGGTATGATGAAGTG AAAGATTATGCTTTTCCATATCCCCAGGATTGCAACCCCAGATGTC CTATGAGATGTTTTGGTCCCATGTGCACACATTATACGCA 312 CODING ATCTGTGTGGCGACGTGCAGTTTACTTGGTATGCAACTATGCCC 313 NON_CODING GCTGTATATTGATGGTCCTTTTGGAAGTCCATTTGAGGAATCACTG (NON_UNIQUE) AA 314 NON_CODING GTCTTCGTTTGATTACTGCCAGTTATTTCCAGCATGCTAAATCCCTA (NON_UNIQUE) CCCACGTTCCAGCCTCTAGGTGAGTCAGTGCGTCACTCTGTCTCCCG TCCAATTAATTATTTCTCATCACTCCCTCAATCCAAGTAACAAACCT TGAAACACGAACATAGACACCAGGCTTATTGGGGCGTGCACAGCC AAGAC 315 CODING CCCGTTGGCTGATTACTCGGAAGAAAGGAGATAAAGCATTACAGA TCCTGAGACGCATTGCTAAGTGCAATGGGAAATACCTCTCATCAAA TTACTC 316 NON_CODING CATTTGGGGCAAATGGTTCACATTCATTTTAGGGTTAGTGGTCATG (UTR) CTGTTTATTTTTCTCTGCTATACAAAGTTCCTCTTAGGGGTCTGCCT CATGACACTAAAAAATGAATAGAGATTCTACTGTAGGTTATCTCCT AGGCTTGAGTTCAACATTTGTTTGGATTTTTGAAGAAAGTCAAATC AAGCAATGCTCCCAAATGATGTCTTTGTAAATTCATACCCTCTGGC CCTA 317 NON_CODING AGATGACAGCGCAAGAGTCAGATTAATGAAAGATCAATAGACATT (INTRONIC) ATTCAGTCTTGAAAAAATTGTGAACAGGGATGCAGGGATCAGTGG GACAATATCAGAAGCTCTAATACATGTTGTCATAGGATGGGGTGGG GGTGAATGAAAAAATAATGGCTGAAAATATCCCAAATTTGATGAA TGATATAAATGTAGAGTCAAGAAGCTCAATCA 318 CODING ACGGAACAAAGGATGAGCAGCCCGAGGG 319 NON_CODING TTGGCACCAATCCTAGACTCACGTGTGCCCCAGAATAACATTCAGA (NON_UNIQUE) CTCTCAGCTGGTCTTGTGTTACACATCCATGGACCGGTTCACTCCAT CATATACAGCTCTCTGCTCCGTGTCCCCTGGGCTCAAGTCAAGCAG TCGGTGACAGATTTCATTCCCAATAACAGAATCGGTTTGCATGACT CCCCATACATGTTGCAGCTTTGAAAACATTCATCTCAGAGTTAGGT ATAAAGACATAAAAATGTGTGTCAAGCCCTCGTTAGCTGATGAGGT AAATGCATGGACAACTTCCTAGGACTTCTCGGCTCTGC 320 NON_CODING ATCATTGAAGGAGACATGGGATGCACAGAGGAACGAGC (ncTRANSCRIPT) 321 CODING AGGACGGGAACACCACAGTGCACTACGCCCTCCTCAGCGCCTCCTG GGCTGTGCTCTGCTACTACGCCGAAGACCTGCGCCTGAAGC 322 CODING TGCGAGAGTCTCTTTGCAAATCGAAGAAGGGAGACATGTTGGGAG CAAGCCCCCCAGAGTCTGGCCATAAACTGGCCCCAAAACTGGCCAT AAGCAAAACCTCTGCAGCACTAAAACATGTCCATAATGGCCCTAAC GCCCAATCTGGAAGGTTGTGGGTTTATGGGAATGAGAGCAAGGAA CACCTGGCCTGCCCAGGGCGGAAAACCGCTTAAAGGCATTCTTAAG CCACAAACAAAAGCATGAGCGATCTGTGTCTTACGGGTGTGTTCCT GCTGCAATTAATTCAGCCCATCCCTTTGTTTCCCATAAGGGATACTT TTAGTTAATTTAATATCTATAGAAACAATGCTAATGACTGGTTTGCT GTTAAATGAAGGGGTGGGTTGCCCCTCCACACCTGTGGGTGTTTCT CGTTAGGTGGAACGAGAGACTTGGAAAAGAGACACAGAGACAAAG TATAGAGAAAGAAAAGTGGGCCCAGGGGACCAGCATTCAGCATAC AGAGGATCCACACTGGCACCGGCCTCTGAGTTCCCTTAGTATTTAT TGATCATTATCGAGCATGGCAGGATAATAGGATAATAGTGGAGAG AAGGTCAGAAGGTAAACACATGAACAAAGGTCTCTGCATCATAAA CAAGGTAAAGAATTAAGTGCTGTGCTTTAGATATGTATACACATAA ACATCTCAATGCCTTAAAGAGCAGTATTGCTGCCCGCATGTCATAC CTACAGCCCTAAGGCGGTTTTCCCCTATCTCAGTAGATGGAAGTAT ATTCCATGTAAAGTAAATCGGCTTTACACCCAGACATTCCATTGCC CAGAGACGAGCAGGAGACAGAAGCCTTCCTCTTATCTCAACTGCAA AGAGGTGTTCCTTCCTCTTTTACTAATCCTCCTCAGCACAGACCCTT TATGGGTGTCGGGCTGGGGGATGGTCAGGTCTTTCCCTTCCCACGA GGCCATATTTCAGACTATCACATGGGGAGAAACCTTGGACAATACC TGGCTTTCCTAGGCAGAGGTCCCTGCGGCCTTTGCAGTATTTTGCGT CTCTGGGTACTTGAGATTAGGGAGTGGTTTGAGATTAGGGAGTGGT GATGACTCTTAAGGAGCATGCTGCCTTCAAGCATTTGTTTAACAAA GCACATCTTGCACAGCCCTTAATCCATTTAACCCTGAGTTGACACA GCACATGTTTCAGGGAGCACAGGGTTGGGGGTAAGGTTACAGATT AACGGCATCTCAAGGCAGAAGAATTTTTCTTAATACAGAACAAAAT GGAGTCTCCTATGTCTACTTCTTTCTACACAGACACAGTAACAATCT GATCTCTCTTTTCCCCACAGTTAATAAATATGTGGGTAAATCTCTGT TGGGGGCTCTCAGCTCTGAAGGCTGTGAGACCCCTGATTTTCTACTT CACACCTCTATATTTTTGTGTGTGTGTCTTTAATTCCTCTAGCGCTG CTGAGTTAGTGACCGAGCTGGTCTCGGCAGAGGTGGGCGGGTCTTT TGAGTTCAGGAGTTCAAGAGCAGCCTGGCCAACATGGTGAAACCC CTTCTCTACTAAAAATATGAAAATTATCCGGGCATGGTGGTGTGCC TCTGTACTTTCAGCTACTCAGGAAGCTGAGGCACAAGAATTGCTGG AACATGGGAGGTGGAGGCTGCAGTGAGCTGAGATCATGCCACTGC ACTCCAGCCCAGGCAATAGAGTAAGACTCTGTCTCAAAACAAAAA GAGTTTTAGGCCAGGTGTGGTGGCTCACGCCTGTAATCCCAGCACT TTGGGAGGCTGAGGTGGGCAGATCACCTGAGGTCAGGAGTTCGAG ACCAGTCTGGCCAACATGGCGAAACCCCATCTCTCTCTACTAAAAA TACAAAATTTAGCCAGGTGTGGTGGTGGGTGCCTGTAATCACAGCT GCTTGGGAGGCTGAGGCAGGAGAATTGGTTGAACCCAGGAGGCAG AGGTTACAGTGAGCAGAGATCGTGCCACTGCATTCCAGCCGGGGTA AGAGAGCGAGACTCTGCCTCAAAAAAAGAAGGCTTAGTGTGCAAC TCATCAGAGTTGCACAGGGCAGAGAAAGAATGGGAAAAAAACAAT TTCTAGAAAACTTTTCGAATTTTCTGATCAACACCAAATATTCCAAA TAGGAAAAATACAAAAAAATCCATACCTATATGTGGCATAATATG ATTGTAGAGCACCAAAGTAAAAGATCTTATTTTTTATTAAAATTAA AAAAAAATTAAAATAGAGGGTCTCACTATGCTGCCCAGGCTGGTCT TGAACTCCTGGCTTCAAGCTATCCTCCCACCATGGCATCCTAAAGT GCTGGGATTGCAGGCATGAGCTGCTGCATCTGGCCCAAAGTAAAA GATCTTAGAAGCGGCCAGAAAAAATAGATTTGGGCTGGGCATGAA TAGATTGATCACCAAAAAGGTGGCAGACTAACTTCTCGACAGA 323 CODING TTTTTGGCATCTAACATGGTGAAGAAAGGA 324 CODING GCTGTGGAGCCTTAGTTGAGATTTCAGCATTTCC 325 CODING GTATATGGACGACTTCTTACTCATGTTAGCCCATTCATTTCATCAGA GCATCTTCACACATCAGTGTTCACTCTCTATAGATTTATTTGCATAT TGTCTAAATATGTTTTTTTCTGTTATTATTTTACACTTTTTATTTTGCT TCATTCTCTGTTGAGTTCCTCA 326 NON_CODING CTTGAGTCCTGGAATCGACCTTTTCTCCAAGGAGCCTTGTTCCTTTT (ncTRANSCRIPT) AGTGGGGAAAGGTATTTAGAAGCTAAGATCTTGGTGTTGGCTGTGT TCACTACAATTGGTGTATCTACTTCTCCATCCTCCAGCGTCCTCTGG TGATCGAGAATCTGAAGTTCCAGGTTTTCATAGGCC 327 CODING GGGTTTGCTGTTTGGATCAAGGAATCAATGGATTGCCAGA 328 CODING GATGGAGAGCATAAGCCATTCACTATTGTGTTAGAAAGAGAAAAT GACACTTTGGGATTCAATATTATAGGAGGTCGACCAAATCAG 329 NON_CODING CAGGCATTCTGATTTATTGATTGTGG (ncTRANSCRIPT) 330 CODING TCTTCATCTTGTCTTACGCTTTCCGAGCAAGTTCAAACCAGAA 331 CODING GCACCAACAAATGTGGTTGCTCCATAATGGAGAGAATGTCAAGAA TGTTGACTATCTTTAGACCTGCTTCATTAATAGATAAGA 332 CODING AACCGCATGCACGAATCCCTGAAGCTTTTTGACAGCATCTGCAACA ACAAATGGTTCACAGACACGTCCATCATCCTGTTTCTTAACAAGAA GGACATATTTGAAGAGAAGAT 333 CODING CAGGCCCAAGTGCATACTCGGGTTCTTTCCAACTCAGAATCATCTC TGATTCCACAAAAGTGAGTTTAGTTTCCTATCTGAATTAACAACTTT AAAGGAGACTATAATAGTTAAAAGTGGAAGAATAGAAATAAATAA ATTTAAAATGAAATTAATTAAAGTAGAAGAGAAGGGTTCTGTTCCA TGTACGATTAATGTGCC 334 CODING CCTGGCATCTATTTCCTCTGTGCAAAGGGAACCATGTATATGAGCT TATAAATAC 335 NON_CODING CTCTTTGGCGTTGCTAAGAGACTGCCAT (ncTRANSCRIPT) 336 CODING TTCCTACCGCATGCATTTTCTAATGTTTGGGGTGGATGGTGTGTCGG TTATGGAAGGCATAGACGTCATTACAGGTGCTACGATCTCACACAC ACACAAGGAAATGTTAGTCTCCTTATTTTATGATTGGAAAATCAAT GACCTAGAGGCAAAATGGCATGTTTAAGGACCTGGGATGACAAGT CATTCTGCAGTCAGCCACAGAGCCAAATTTGGACTCCTCAACCAGA ACTCCATGAAAAGCCTGACTTTGCCAAACACTGTGCTGGAAAAGCT AAGCCCCTTTCATTTGTGAAGTAAATTTTAAATTCAAGATATTTAGT TTAGAGAATTGAGTCTTGAGATGTAAACTACATGAGATTTCTTTGG TTTCAATTGAATAATATTCACTAACAAATGATTTACTAAAATACGT ATTTCTTGGTCCTTATCATGTAATGACAGATTCACAACAGCAATAA GGATGGAGATTTCCCCAATAATTAATAACACCGAGAGTAGCAATAT TTTTTA 337 NON_CODING GTAGAGCCTACGTCCTTCATGAGAAAAATGACACAAATCTCAGTAT (ncTRANSCRIPT) TCTTTGTTTGGAGTCTCTTGACATCCATGTGAG 338 NON_CODING TTAGGACACGGACATTTCTATTTGGCAGCCAACA (ncTRANSCRIPT) 339 CODING CGGAAGACTTGCCACTTTTCATGTCATTTGACATTTTTTGTTTGCTG AAGTGAAAAAAAAAGATAAAGGTTGTACGGTGGTCTTTGAATTAT ATGTCTAATTCTATGTGTTTTGTCTTTTTCTTAAATATTATGTGAAAT CAAAGCGCCATATGTAGAATTATATCTTCAGGACTATT 340 NON_CODING GCTTCTGTCCCAAGAGGCACTAGCTGGGG (ncTRANSCRIPT) 341 CODING AACATTGGAGAAGTATCTCTTTGTAATGCTAAAAAGAAGTGAAAAT CAACAGACTTATCTAATGAATGCAGATGTGGCAGAAAGAATGAGT AGCACTACCGTTGACTCTGAAGAGAGA 342 CODING ACTACTAGACTTGCTAAACTTGGACTGTTGTGAATTAGAACCTAAA ATTGAAGAGATTAATATTAGGCGCCTATATTTTGCTTCTAAATCAA GAAATAAAATTATTAGCAGTATGGTTTCTTTTACTGATGAACATGTT TGTATTGAACAAGGAACACATACTAATATCTATTGAGTGCCTACTA TGTGCTAATCTCCAACAAATTGATTTGGGGATGCTAAGAAGAATTA TGTGCCAGTGTTACCCTCAAGGAGCAATACTGTATATA 343 NON_CODING AATCTCATCTCTATGACATCCCTATCCTG (ncTRANSCRIPT) 344 NON_CODING CTCAGTCTATGAAAGCCAGGTTAGCTTGCTTTCTTCCTCCCTAAATC (ncTRANSCRIPT) CTCCATCCTCATGACCAACAAAGAAATAGTTGAATCATTTTCCAGG CACATCTTGGGGAGGATGTGGGGCCATTGGAGGCTGTCCTTCCTAG ATAAGTCTTTAGGAGTGAGAACAAGGAGTCTTACCCTCCTCTGTCC ACCCACCCCCATGAATGGGCCTGGCTCCAGCCAGGAGTTGTGGTTT TTCCTGAGCTCCTCACCTATCTCTTCTGGATTTCACATTGGCAAACG GGGTTGCAAAGTGCTCTTCGTGCTCTTTGGACAGTGCC 345 NON_CODING TGGTTGCATTGCACGTAGAAAGTGGAATAATGTAATGAGCTTTGAA (ncTRANSCRIPT) ACCATAATAATGAATGTCTGAATAATGACATTATTTCTTGCGTTTGT AATACTGTTAATTAAATCTATGTCGATCCTGTTGGAATTCATAAAAT CATCTAAAAATTTTTCTAAATATACAGTGTTGTTTTCCCCATTGTAT CTTGATCTCAAGCAACAAATGGTAAAAGTATAGCTATTAATGTCAT TAAATGTGAATTGTTTCAACATTATGAAGGGTTCCTCTTGGTAAGT GGCAGAAGGAGCCAGGCTTAGGTTTGAAGTGAGACTGACTTTATTC CCTTCTT 346 NON_CODING CTCCTGAATGCTGGCCAGACAAATGGAAATCTGCCAGGGTTGGGTA (ncTRANSCRIPT) CCCCCATGACAGCAGCCAGCCTGCCCTCTTAGTCCCTGACAGCTGC AGTGACAGCATCTGTGATTGCAAAGCGTGACAATTTATATCTCTCA TTTCATCACACCATCTATCAGCAGACAGTCAGGCTTTAAAAATCAA TCCCACACTGACTCAGTCCCCAGCAGAGATGGCCTCTGACAACAGT ATCCACACTGCAGGCTGGACAAGGGCCCTATTAATTTTGAGACTCA GCCAAATTTCCTTCTGACCCTAAGCTGGTGAATCCCTGCTCCTTTGC TTTGGTTGGGGTTGGTGTGAGCTAAGGCTGTGATCCCATTTGCTCCT ATGGCCTCCAGGTGGCCTGGGCCTCCATGAATGGGCCACATGGTCA TACTGAATGCTTGATTACACTCAGACCTAGCAGTCGTCTGGGCGCA GCTGGTTTATGGATCACTTT 347 NON_CODING ATGGCCTTTGAATCATACTTAAGTTT (ncTRANSCRIPT) 348 CODING ACCGAGGAGGAGATTCTCTTTAATTATCAAAGACACATCTTTTCAG GGGGCCAACAAAGCATTTATTTCACCCGCCAAACTAAAGGAGAGTT ATTCCAGTTTAGGAGGAAGATGCAAGCGGTTTGGGACCTTGAACA 349 NON_CODING TGGAGGCTAATCTTGTTTGTTATACTTTAGTCATTAATTCAAAGTAA (ncTRANSCRIPT) AGGAGTTGTTAATGAACTGGAAACTCCTTTTGAATTATGGTAGCAA TCAGAATATTTTTATATTAGCCAGTTTTACCTTGAAGACCTATTTTT AAAAACTACCTGTGTCTCTGGACTTAGTTGCAAATGCATATTAAAA CAAAAATCCCCCAATTTCTGTGCTTTCTTATTTGAAAGGCCATTTCT AGGGGGAAAACAGTTCCCAAACACATTATACATGTTGGAAAAGTTT ATCTCTAACCTTTTGAATTAAACAATTTCAGAATTGAAAACAGTAA GGTGAATTTTAGGCCAATAACTCTTTTCTATAATCTTGACTCTTTTA AGATTAGGCAGTTCAGATAGTCTTATACTA 350 NON_CODING ACCTAGTTGGCTTTCATCTAATTCATTGCCATTTTAAGTGTGTATTA (ncTRANSCRIPT) TTTTAGAGCAAACTTAGAAAAACAGCACATTTCTAGTAACTTACGA CATTCGATGAATGATAAATGTTCAAGTTAGACTAAAGGAACTTTAT TCCAACTTCTAGTAACTACTTTCTTCA 351 CODING AGCATTATCTAAACTGCAGTCACTGTGAGGTAGACGAATGTCACAT GGACCCTGAAAGCCACAA 352 NON_CODING TTCACAGGACTTCGCCACGCTGCTTTGGAATCTTTCACACCCCCCTA (ncTRANSCRIPT) CCCCCAGATACCTTTGAAAAATTTGAGGTTCCTGTTCCTTGTTTCTC AGTGTATTCATTTCTTCCCTGACTATGACATGTTAAAAAA 353 CODING CTTCAACGATGAGAAGTTTGCAGAT 354 CODING GGAAAGACGAGAACTATTTATATGACACCAACTATGGTAGCACAG TAG 355 CODING TGCCCCTAGATCTGACAGTGAAGAG 356 CODING GCAGCAGTCCCAAATAGTCAAAATGCTACTATCTCTGTACCTCCAT TGACTTCTGTTTCTGTAAAGCCTCAGCTTGGCTGTACTGAGGATTAT TTGCTTTCCAAATTACCATCTGATGGCAAAGAAGTACCATTTGTGG TGCCCAAGTTTAAGTTATCTTA 357 CODING GTGGTGTATGCGGATATCCGAAAGAATTAA 358 CODING GAAGTTCAGAAGCTACAGACTCTTGTTTCTG 359 CODING GAAGCTTCTGCAGTTCAAGCGTTGGTTCTGGTCAATAGTAGAGAAG ATGAGCATGACAGAACGACAAGATCTT 360 CODING CTGTTGCTGAAACTTACTATCAGACAG 361 CODING GCTCAGAAAAAGAAGTTCGAGCAGCAGCACTTGTATTACAGACAA TCTGGGGATATAAGGAACTGCGGAAGCCA 362 CODING CTTACCAGCGTTATAGGCCAGTATCAACTTCAAGTTCAACCACTCC ATCCTCTTCACTTTCTACTATGAGCAGTTCACTGTATGCTTCAAGTC AACTAAACAGGCCAAATAGTCTTGTAGGCATAACTTCTGCTTACTC CA 363 CODING TGTGCAAGTAGTACTCGATGGACTAAGTAAT 364 CODING TTGCAAATTCCATATCTACAATGGTACACGTCCATGTGAATCAGTTT CC 365 CODING CTGGCCAGTGATTCACGAAAACGCAAATTGCCATGTGATACT 366 CODING TTGGATGACTGCAATGCCTTGGAAT 367 CODING CTTCTTCCTGAATCACGATGGAAAAACCTTCTTAACCTTGATGTTAT TAAG 368 CODING TCCTCGTTTTATCCTGATGGTGGAG 369 CODING TTTTTGACAACAGGTCCTATGATTCATTACACAG 370 CODING GGACCACTGCATGGAATGTTAATCAATACTCCATATGTGACCAAAG ACCTGCTGCAATCAAAGAGGTTCCAGGCACAATCCTTAGGGACAAC ATACATATATGATATCCCAGAGATGTTTCGGC 371 CODING AACCTGTAAGTGTAATGGCTGGAAA 372 CODING CGCCCTATTAGGAGAATTACACATATCTCAGGTACTTTAGAAGATG AAGATGAAGATGAAGATAATGATGACATTGTCATGCTAGAGAAAA AAATACGAACATCTAGTATGCCAGAGCAGGCCCATAAAGTCTGTG 373 CODING AAACCTAAGACTTGTGAGACTGATGC 374 CODING CATGAACGGGGACCTGAAGTACTGA 375 CODING AGTTTTTACAGATTACGAGCATGACAAA 376 CODING TCCCTCTTATTCTGGAAGTGATATGCCAAGAAATG 377 CODING AGACCTGGATTTTTTCCGGAAGATGTGGATTGACTGGAA 378 CODING TAAAGATGATAATCAGGAAATAGCCAGCATGGAAAGACA 379 CODING AGCAGTGATAATAGCGATACACATCAAAGTGGAGGTAGTGACATT GAAATGGATGAGCAACTTATTAATAGAACCAAACATGTGCAACAA CGACTTTCAGACACAGAG 380 CODING TTCAGAACAAGAGCTAGAGCGATTAAGAAGCGAAAATAAGGA 381 CODING AAGAACCAGATGACTGCTTCACAGA 382 CODING GTCGGCAGGTTCTAAAAGATCTAGTTA 383 NON_CODING ACCTTGCAACGGATGTCCTTGTTGATCAGCACGTTCTTGCCCTTGTA (CDS_ANTISENSE) GTTGAAGATGACATGA 384 NON_CODING ATGATGATGCTGTTAACTACATTCAACAAAAATCCTTTAAAACAGC (CDS_ANTISENSE) TGTTTTCAACCAACTTTCGCTGTGAATGTACTTTT 385 NON_CODING CTGCCAGCTGAATCAACAGGGTAAA (CDS_ANTISENSE) 386 NON_CODING CCATCTTCAAGTTTGGACTCATAGACTTGGGTTAAAGATTTTACTTT (CDS_ANTISENSE) TTGCTCCATTTCACTATTTTGTTTT 387 NON_CODING TGGGTCTTCTCTTCAAGCAACAGAC (CDS_ANTISENSE) 388 NON_CODING GCATTTTGAGGACTTCGTTTGGATCCCAATTCAAACAAAATAACTG (CDS_ANTISENSE) TGAAGAGATTTTTTCGAACAACAGAGGAGATTCAATTACACACTGG GTTACATGATCTGAAGGAACTGGCATTTTTTTAAATGTGTGATAAC GGCACTGA 389 NON_CODING AGGGTGATTAGGAATTAACTGGACAAAGAAGAGGGAAAGTCTTTG (CDS_ANTISENSE) CAAGTAGAGGAAAGAATCTGCTTGGAGCTCAGATAACTATTATTTG AAAACATAATGACATCTAGTTCAAACTTGTGACTGAGTTCCACAGT AGAATTCACAGAAAAAAAATTATTAAATATAATATTTCCATCAGTC TGTGTCTAAAAGATTAAAAAAGAGCAAATAACAATCTTAATAAACT GATGATAGATTATAGCCTCATCTCTTCCAACATCCGATTCTGTG 390 NON_CODING GAAATGTTCAAGATGGTCAGGAAAG (INTERGENIC) 391 NON_CODING CCTGTTTCCTCTCGATATGCTACAG (INTERGENIC) 392 NON_CODING CTGTTCATCCTGCTGTAGATCTGTT (INTERGENIC) 393 NON_CODING AAATGTTGACAATTGGGACGATGTAAATGTAAAG (INTERGENIC) 394 NON_CODING GCAAAGGTGTCCAAATTATGCAGAC (INTERGENIC) 395 NON_CODING AGTTATAACATGAAGGGATTTTCATCTTTTGCTGTATGAAGGATAA (INTERGENIC) TTGTTATATCACATTTGGGGGGTAATAACA 396 NON_CODING CAAAACGACTCACTGGGTTTTTCAT (INTERGENIC) 397 NON_CODING AGAGAAAGTGAAGATTCGATTTGAG (INTRONIC) 398 NON_CODING TCAGAATTAAACCTGTGGCCCAGGT (INTRONIC) 399 NON_CODING TGCCAAAGATTAAGGGGAGCCTTTG (INTRONIC) 400 NON_CODING CGTCCGATTAGTGCCATGGCTGGCA (INTRONIC) 401 NON_CODING CTCATGGGAAGGGAACTCCGTGTCA (INTRONIC) 402 NON_CODING AGAGTTATGAAGGAACAGGTTGTCCTTGTCTGGAGTCAAGCTAAAC (INTRONIC) ACATGATTTGT 403 NON_CODING GGATAGGAATAAAGCAAGACAGTTA (INTRONIC) 404 NON_CODING TAAGATCTGTAACACTGAGGAAGTACCAATAAAGAGCTGCTAACA (INTRONIC) CT 405 NON_CODING AGGACAAGAGCCCTAGAGTGGCCTG (INTRONIC) 406 NON_CODING GCAGATACACGTGGACAAAAGACTT (ncTRANSCRIPT) 407 NON_CODING GTAACACAGCAGGAGCTCATGTTTT (ncTRANSCRIPT) 408 NON_CODING ATGCCTACAATTCCTGCTACTTGAG (NON_UNIQUE) 409 NON_CODING ATTGGCTTTTAGTTTATCAGTGAATAA (NON_UNIQUE) 410 NON_CODING TCTCTGGGGGAATTTCATTTGCATCTATGTTTTTAGCTATCTGTGAT (UTR) AACTTGTTAAATATTAAAAAGATATTTTGCTTCTATTGGAACATTTG TATACTCGCAACTATATTTCTGTA 411 NON_CODING TCAGAAGTCGCTGTCCTTACTACTTTTGCGGAAGTATGGAAGTCAC (UTR) AACTACACAGAGATTTCTCAGCCTACAAATTGTGTCTATACATTTCT AAG 412 NON_CODING CTTACATACCGTGAGAAGTTACGTAACATTTACTCCTTTGTAAATGT (UTR) TTCCCTATCATCAGACAAA 413 NON_CODING CACTTCATATGGAGTTAAACTTGGTCAG (UTR) 414 NON_CODING TGTACTTTTCAGAATATTATCGTGACACTTTCAACATGTAGGGATAT (UTR) CAGCGTTTCTCT 415 NON_CODING CACTGTTGTAGTAAAGAGACATATTTCATGAATGGCATTGATGCTA (UTR) ATAAATCCTTTGC 416 NON_CODING GGAGCACTACCATCTGTTTTCAACATGAAATGCCACACACATAGAA (UTR) CTCCAACATCAATTTCATTGCACAGACTGACTGTAGTTAATTTTGTC ACAGAATCTATGGACTGAATCTAATGCTTCCAAAAA 417 NON_CODING (UTR) CTGAAATGAGACTTTATTCTGAAAT 418 NON_CODING TTTTGTACAACAGTGGAATTTTCTGTCATGGATAATGTGCTTGAGTC (UTR) CCTATAATCTATAGAC 419 NON_CODING TGTTTTTCCGCAATTGAAGGTTGTATGTAA (UTR) 420 NON_CODING CCTTGCATATTACTTGAGCTTAAACTGACAACCTGGATGTAAATAG (UTR) GAGCCTTTCTACTGG 421 NON_CODING TTCTCTTCTTTAGGCAATGATTAAGTT (UTR) 422 NON_CODING CCACTGGCCTGTAATTGTTTGATATATTTGTTTAAACTCTTTGTATA (UTR) ATGTCAGAGACTCATGTTTAATACATAGGTGATTTGTACCTCAGAG TATTTTTTAAAGGATTCTTTCCAAGCGAGATTTAATTATAAGGTAGT ACCTAATTTGTTCAATGTATAACATTCTCAGGATTTGTAACACTTAA ATGATCAGACAGAATAATATTTTCTAGTTATTATGTGCAAGATGAG TTGCTATTTTTCTGATGCTCATTCTGATACAACTATTTTTCGTGTCAA ATATCTACTGTG 423 NON_CODING TACAAGCTTATTCACATTTTGCTTCCTAATCTTTTTGTTGTACAGGG (UTR) ATTCAGGTTTCTTATTCTTACAACATGATTGTTTATATGTGAAGCAC ATCTTGCTGTTGCCTTATTTTTGATGCTTTTATTCATGACAAGAA 424 NON_CODING ACAGAATCAGGCATGCTGTTAATAAATA (UTR) 425 NON_CODING TCTGATTTCATTGTTCGCTTCTGTAATTCTG (UTR) 426 NON_CODING CAAGCTGATGATTGTTGCATTTTGGAGTTGCAACAACATTAAAACA (UTR) 427 NON_CODING GGCCATGTGCTTTAACGTTACGGTAATACTTTACTTTAGGCATCCCT (UTR) CCTGTTGCTAGCAGCCTTTTGACCTATCTGCAATGCAGTGTTCTCAG TAGGAAATGTTCATCTGTTACATGGAAAAAATGTTGATGGTGCATT GTAAAATTA 428 NON_CODING TGCTGGTTTAAGATGATTCAGATTATCCTTGT (UTR) 429 NON_CODING TGAATGCGTGACAATAAGATATTCC (UTR) 430 NON_CODING TGGCCCAGAAAGTGATTCATTTGTAA (UTR) 431 NON_CODING GACAACCCGGGATCGTTTGCAAGTAACTGAATCCATTGCGACATTG (UTR) TGAAGGCTTAAATGAGTTTAGATGGGAAATAGCGTTGTTATCGCCT TGGGTTTAAATTATTTGATGAGTTCCACTTGTATCATGGCCTACCCG AGGAGAAGAGGAGTTTGTTAACTGGGCCTATGTAGTAGCCTCATTT ACCATCGTTTGTATTACTGACCACATATGCTTGTCACTGGGAAAGA AGCCTGTTTCAGCTGCCTGAACGCAGTTTGGATGTCTTTGAGGACA GACATTGCCCGGAAACTCAGTCTATTTA 432 NON_CODING GTTAATATTGTCATCGATACAAATAAAGTGAAAT (UTR) 433 NON_CODING CAATAACTGTGGTCTATACAGAGTCAATATATTTT (UTR) 434 NON_CODING GTCGCCTGCGAGGCCGCTGGCCAGG (UTR) 435 NON_CODING CAGGCCTTCTGCAAATCAGTGCTGG (UTR) 436 NON_CODING TAAGGATGGAATTCAACTTTACCTA (UTR_ANTISENSE) 437 NON_CODING TACACGTAAACCACAAAAGAGTAGCATTCCATTTTCTTGAAGTGCA (UTR_ANTISENSE) CATGATATTATGAACAATACAAATGCATTATTTTTATCATTAATAGT TTAATCATTAATTATCTCATAAGTCAATGCAGAGAGTGAA 438 NON_CODING CTCACTTATTTAACTGGCAACTATCCATTTAGGTTAGGCAAAGGCA (UTR_ANTISENSE) CGGTAACATGTTGCGCAGGATGTTTTACTGA 439 NON_CODING CAGGGGTATGGAACATGCTGTCATATTTCATTCATAACACACATGT (UTR_ANTISENSE) ACTATAGCTCTAGGCAACAGATGGACAATCGCTTGTTTGAACTACA A 440 NON_CODING CCACATGGTCATCATTAGCCAGCTG (UTR_ANTISENSE) 441 NON_CODING CTTTTGGATGTGATAAGCTTTGTAATTGTCTTTTAATGAGCTCTCAT (UTR_ANTISENSE) CTTGGAGAGATACATTCT 442 CODING GTGATCGCCTACTACGAGACAAAAA 443 CODING ATTTATCTTCCACTGAATTGGCAGAAA 444 NON_CODING GTCAGGTAAACATGTATGTTCAGTCCTTCACTA (INTRONIC) 445 NON_CODING GGAACTATGAACTTGCCTATCTAAC (INTRONIC) 446 NON_CODING ACATGGAATGACTTAGTTACAGACCAGACATATTGTTACTGGGAAT (INTRONIC_ G ANTISENSE) 447 NON_CODING AGAGGAATGTTTGCTACCTTTAGCGGTGAAAAAAGAAAGAGAGTC (UTR) AAGAATTTTGTTGGATTGTGTTTGTGTGTGCATATATTTGATATCAT CATTATATTTGTAATCTTTGGACTTGTAATCATAGCCTGTTTATTCT ACTGTGCCATTAAATATACTTTACCTTA 448 NON_CODING AAGTAATGAGCACTTTCTACTCAAGC (UTR) 449 CODING CATCCCTAGCACAGATATCTACAAAA 450 CODING GTCCATCAGGATTCAAACTGTAATGGCATTTGG 451 CODING AGTTTCTTGTCTTCTACAACAATGATCGGAGTAAGGCCTTTAAA 452 CODING ACACAAACGTATATCGTATGTTCTCCAAAGAG 453 CODING TTGACCTCAAATGCAGTGAGTTCTG 454 CODING GGGCGTGATAGTGCACGCCTACAAA 455 CODING GTGAGGGAATATGTCCAATTAATTAGTGTGTATGAAAAGAAACTGT TAAACCTAACTGTCCGAATTGACATCATGGAGAAGGATACCATTTC TTACACTG 456 CODING TCTAGGACGAGCTATAGAAAAGCTATTGAGAGTATCTAGTTAATCA GTGCAGTAGTTGGAAACCTTGCTGGTGTATGTGATGTGCTTCTGTG CTTTTGAATGACTTTATCATCTAGTCTTTGTCTATTTTTCCTTTGATG TTCAAGTCCTAGTCTATAGGATTGGCAGTTTAA 457 CODING TTGCTTTGATCGTTTAAAAGCATCATATGATACACTGTGTGTTT 458 NON_CODING (INTRONIC) TATTCAATCTCTGGCACAATGCAGCCTCTGTAGAAAAGATATTAGG 459 NON_CODING ATGCAGCAATGCGTGCTCGACCATTCAAGGTTGAT (ncTRANSCRIPT) 460 CODING TTCAACTGCAGCTCGGGCGACTTCATCTTCTGCTGCGGGACTTGTG GCTTCCGGTTCTGCTGCACGTTTAAGAAGCGGCGACTGAACCAAAG CACCTGCACCAACTACGACACGCCGCTCTGGCTCAACACCGGCAAG CCCCCCGCCCGCAAGGACGACCCCTTGCACGACCCCACCAAGGAC AAGACCAACCTGATCGTCTACATCATCTGCGGGGTGGTGGCCGTCA TGGTGCTCGTGGGCATCTTCACCAAGCTGG 461 CODING GGCCTACTGTGAAGCTCACGTGCGGGAAGATCCTCTCATCATTCCA GTGCCTGCATCAGAAAACCCCTTTCGCGAGAAGA 462 CODING TCACTGAATTTTAACCGGACCTGGCAAGACTACAAGAGAGGTTTCG GCAGCCTGAATGACGAGGGGGAAGGAGAATTCTGGCTAGGCAATG ACTACCTCCACTTACTAACCCAAAGGGGCTCTGTTCTTAGGGTTGA ATTAGAGGACTGGGCTGGGAATGAAGCTTATGCAGAATATCACTTC CGGGTAGGCTCTGAGGCTGAAGGCTATGCCCTCCAAGTCTCCTCCT ATGAAGGCACTGCGGGTGATGCTCTGATTGAGGGTTCCGTAGAGGA AGGGGCAGAGTACACCTCTCACAACAACATGCAGTTCAGCACCTTT GACAGGGATGCAGACCAGTGGGAAGAGAACTGTGCAGAAGTCTAT GGGGGAGGCTGGTGGTATAATAACTGCCAAGCAGCCAATCTCAAT GGAATCTACTACCCTGGGGGCTCCTATGACCCAAGGAATAACAGTC CTTATGAGATTGAGAATGGAGTGGTCTGGGTTTCCTTTAGAGGGGC AGATTATTCCCTCAGGGCTGTTCGCATGAAAATTA 463 CODING CCAGTTCCAGGCCTGGGGAGAATGTGACCTGAACACAGCCCTGAA GACCAGAACTGGAAGTCTGAAGCGAGCCCTGCACAATGCCGAATG CCAGAAGACTGTCACCATCTCCAAGCCCTGTGGCAAACTGACCAAG CCCAAACC 464 CODING ATGAGTGCCAAATCTGCTATCAGCAAGGAAATTTTTGCACCTCTTG ATGAAAGGATGCTGGGAGCTGTCCAAGTCAAGAGGAGGACAAAGA AAAAGATTCCTTTCTTGGCAACTGGAGGTCAAGGCGAATATTTAAC TTATATCTGCC 465 CODING GGTTCTGCTCCTCGACGGCCTGAACTGCAGGCAGTGTGGCGTGCAG CATGTGAAAAGGTGGTTCCTGCTGCTGGCGCTGCTCAACTCCGTCG TGAACCCCATCATCTACTCCTACAAGGACGAGGACATGTATGGCAC CATGAAGAAGATGATCTGCTGCTTCTCTCAGGAGAACCCAGAGAG GCGTCCCTCTCGCATCCCCTCCACAGTCCTCAGCAGGAGTGACACA GGCAGCCAGTACATAGAGGATA 466 CODING TGGTCATCCGCGTGTTCATCGCCTCTTCCTCGGGCTTCGT 467 CODING AGGAAGAACAGAGAGCCCGCAAAGACCT 468 CODING TGCCACCCAGATGAACAACGCAGTGCCCACCTCTCCTCTGCTCCAG CAGATGGGCCATCCACATTCGTACCCGAACCTGGGCCAGATCTCCA ACCCCTATGAACAGCAGCCACCAGGAAAAGAGCTCAACAAGTACG CCTCCTTA 469 CODING ACTGGGGTGACCTTAACCTGGTGCTGCCCTGTCTGGAGTACCACAA CAACACATGGACATGGCTAGACTTTGCCATGGCTGTCAAAAGGGAC AGCCGCAAAGCCCTGGTTG 470 NON_CODING TGGCACAGTCAGATGTCGAGAAACTTTGCTATGCCTCCGAAGTCAA (INTERGENIC) TGCCC 471 NON_CODING CCTCACAATATGGAAAGACGGGACAACCTATGGAACTATCTGTGAC (INTERGENIC) TTCCATGTACCAAGACAAGGACGCTATAGCTAGGGTAGTGAGACC 472 NON_CODING CAGTGGATGAATGTCGGAACCTTATGAAATGTGACTCATCTGACCT (INTRONIC) TTCAGAGATTGGAACTGCCCCACAGTGCTGTTCTGCTAACTCTTCTT CTCTGCCCTCTAAAGTCCCTGCTTCCCTTTCTTTCCTTTTTAGTACCG GGGTGTACATAATCGATCCATCATAATCATCAGTTCATGACATGTT CTCATCATTGATCCATAGCACGGCCTTG 473 NON_CODING CCTGCAAAGTAAGGTGTATGGGGAAGCAAGTAGATAGT (INTRONIC) 474 NON_CODING GCTGATCTCACTGTGATCTTCCTGGTGTT (INTRONIC) 475 NON_CODING GACTCGAGAAAAAACAGAGCTCAGACTTGAGACACGGGCTTCCCT (INTRONIC) CTATAGGGGTCAAAAACCAGGGCGGAGAGAGATAACCA 476 NON_CODING TTGTACCTGCAGTTTTCGCAGAGTAGATCAAGGACTGCA (INTRONIC) 477 NON_CODING TTGTCTCTCAGTCGGCTAAGTGCTCTCCCACCAGGTCACCTAAAAC (ncTRANSCRIPT) GACCAGCAGAGACACCCAAGAGGCTGAGCTGTGAGGATCACCTGA ACCTGAGCCTGGGAAGTGGAGGTTGCAGTGAGCTGTGATCACACC ACTGTGCTCCAGCCTGGGCAACGGAGTGAAACCCTGTCTCAAGAAA GGACCAGCAGTGACATTTGTTAAATATCGA GGGTGGTTGAACATCCACTATTTATAAGGAAATGTTATTTCCCACA AATCTCATTCCTCAGAAATCAGTGAAAGACAGACCCTGTCTCGGAT TCTATAAAGCAGTGTGACTGATGTGGCCAAAC 478 NON_CODING CAGCGTCCTGGGAATGTCATTTCTGCTCCACTCCTTGGACTCGCTGA (UTR) GCTGTCTCCGCCTCCACCTATCTTCCTACAGACCTCCCTTCTAGTTT TCTGTCAATTCTTTGAGCCAGCAAACTCCATCCAGTACATTCTTTCT TCTTTCATGAAAGAGCTTGAGTTGGATGTAAATATATATGACCTAA CAATTCCACCCCTAGGTGTATACCCTACAGAAATGTGTACATGTGT TCATCCAGAGACATGCTCTAAATCTTCACAAAAACACTCTCCATAA TAACCCCGAACAGGAAAGCACCCCAATGCCCATGTTGGCTGGATA AGCACATTAGGGTATATTCACACGATGGAATCCCAGACTGCAATGG GAATGAGCTGCAACTCCACCCCCAACTTGGAGTGTATTCACCAACC CTAGTGTTGAACGAGATAAGGCAAAAATGCACCATAGGATTCCATT TATATAAAGTTTAAAACCCAGCAAAATTCATCCATGCGGTTGCAAG TAGAGATCAGTCCTAAGAAGACAGTAACCAGAAGCGGGCATGAGG TGGTGCTTCTGGGGTGTTCTGTTTCTTGATCTGGTTGCCGGTTACCT GGGTGCTTTCCGTTTGTGAACATTCTTGGAGCTGTACACTTTTGATC TGGGCA 479 NON_CODING TCTGAATTCACCTCTCATCTGACGACTGACAGCTGCT (UTR) 480 NON_CODING GCAAGCCGCAGAACGGAGCGATTTCCTCCGAGAAAGTTGAGGATG (UTR) GAGCCTTTTTTTCCGCACCGTCCCCGCGATGGCATGGGCCCCGAGA ATGCTGCCCCGAGGCTCCCAGTGTGGGGGAGCTCGGGGTCGCTGCG CCTCTAGCTTGAGCGCAGAAATCCGCGAATCACTCCGATCTTCGCG AACTCTGGCATCTTCTAGGAAAATCATTACTGCCAAAACTGAGGCG AGCTTTTC 481 CODING ACCTGCACTGGCTCCTGCAAATGCAAAGAGT 482 CODING CTGCTGCCCCATGAGCTGTGCCAAGTGTGCCCAGGGCTGCATCTGC AAAGGGGCATCAGAGAAGTGCAGCTGC 483 CODING TGTGTCTGCAAAGGGACGTTGGAGAACT 484 CODING CATGGGCTGAGCCAAGTGTGCCCACGGCTGCATCTGCAAAGGGAC GTCGGAGAAGTGCAGCTG 485 CODING GAAAAGCGTGCAAGTATCAGTGATGCTGCCCTGTTAGAC 486 CODING TGCAATTTCATCAGCACCAGAAAGTTTGGGAAGTTTTTCAGATGAG TAAAGGACCAG 487 CODING CCAGTACAAACCTACCTACGTGGTGTACTACTCCCAGACTCCGTAC GCCTTCACGTCCTCCTCCATGCTGAGGCGCAATACACCGCTTCT 488 CODING TGCTAGCAAACACCATCAGATTGTGAAAATGGACCT 489 CODING GTATCTGGACTCTCTTAAGGCTATTGTTTTTA 490 CODING ACCTTTGAAACTCACAACTCTACGACACCT 491 CODING CCCTCCGATGCCTAATAAAGTTCTCTAGCCCACATCTTCTGGAAGC ATTGAAATCCTTAGCACCAGCGG 492 CODING ACTGCTCACTTGCATACCCAACAAGAGAATGAA 493 CODING GGAAGGACACCACTGGTACCAGCTGCGCCAGGCTCTGAACCAGCG GTTGCTGAAGCCAGCGGAAGCAGCGCTCTATACGGATGCTTTCAAT GAGGTGATTGATGACTTTATGACTCGACTGGACCAGCTGCGGGCAG AGAGTGCTTCGGGGAACCAGGTGTCGGACATGGCTCAACT 494 CODING TTGCTACATCCTGTTCGAGAAACGCATTGGCTGCCTGCAGCGATCC ATCCCCGAGGACACCGTGACCTTCGTCAGATCCATCGGGTTAATGT TCCAGAACTCACTCTATGCCACCTTCCTCCCCAAGTGGACTCGCCCC GTGCTGCCTTTCTGGAAGCGATACCTGGA 495 CODING AGCTGATTGATGAGAAGCTCGAAGATATGGAGGCCCAACTGCAGG CAGCAGGGCCAGATGGCATCCAGGTGTCTGGCTAC 496 CODING ACACGCTGACATGGGCCCTGTACCACCTCTCAAAGGACCCTGAGAT CCAGGAGGCCTTGCACGAGGAAGTGGTGGGTGTGGTGCCAGCCGG GCAAGTGCCCCAGCACAAGGACTTTGCCCACATGCCGTTGCTCAAA GCTGTGCTTAAGGAGACTCTGCG 497 CODING ACAAACTCCCGGATCATAGAAAAGGAAATTGAAGTTGATGGCTTCC TCTTCC 498 CODING GAGTGTGGCCCGCATTGTCCTGGTTCCCAATAAGAAA 499 CODING GGTGCTGGGCCTACTAATGACTTCATTAACCGAGTCTTCCATACAG AATAGTGAGTGTCCACAACTTTGCGTATGTGAAATTCGTCCCTGGT TTACCCCACAGTCAACTTACAGAGAAGCCACCACTGTTGATTGCAA TGACCTCCGCTTAACAAGGATTCCCAGTAACCTCTCTAGTGACACA CAAGTGCTTCTCTTACAGAGCAATAACATCGCAAAGACTGTGGATG AGCTGCAGCAGCTTTTCAACTTGACTGAACTAGATTTCTCCCAAAA CAACTTTACTAACATTAAGGAGGTCGGGCTGGCAAACCTAACCCAG CTCACAACGCTGCATTTGGAGGAAAATCAGATTACCGAGATGACTG ATTACTGTCTACAAGACCTCAGCAACCTTCAAGAACTCTACATCAA CCACAACCAAATTAGCACTATTTCTGCTCATGCTTTTGCAGGCTTAA AAAATCTATTAAGGCTCCACCTGAACTCCAACAAATTGAAAGTTAT TGATAGTCGCTGGTTTGATTCTACACCCAACCTGGAAATTCTCATG ATCGGAGAAAACCCTGTGATTGGAATTCTGGATATGAACTTCAAAC CCCTCGCAAATTTGAGAAGCTTAGTTTTGGCAGGAATGTATCTCAC TGATATTCCTGGAAATGCTTTGGTGGGTCTGGATAGCCTTGAGAGC CTGTCTTTTTATGATAACAAACTGGTTAAAGTCCCTCAACTTGCCCT GCAAAAAGTTCCAAATTTGAAATTCTTAGACCTCAACAAAAACCCC ATTCACAAAATCCAAGAAGGGGACTTCAAAAATATGCTTCGGTTAA AAGAACTGGGAATCAACAATATGGGCGAGCTCGTTTCTGTCGACCG CTATGCCCTGGATAACTTGCCTGAACTCACAAAGCTGGAAGCCACC AATAACCCTAAACTCTCTTACATCCACCGCTTGGCTTTCCGAAGTGT CCCTGCTCTGGAAAGCTTGATGCTGAACAACAATGCCTTGAATGCC ATTTACCAAAAGACAGTCGAATCCCTCCCCAATCTGCGTGAGATCA GTATCCATAGCAATCCCCTCAGGTGTGACTGTGTGATCCACTGGAT TAACTCCAACAAAACCAACATCCGCTTCATGGAGCCCCTGTCCATG TTCTGTGCCATGCCGCCCGAATATAAAGGGCACCAGGTGAAGGAA GTTTTAATCCAGGATTCGAGTGAACAGTGCCTCCCAATGATATCTC ACGACAGCTTCCCAAATCGTTTAAACGTGGATATCGGCACGACGGT TTTCCTAGACTGTCGAGCCATGGCTGAGCCAGAACCTGAAATTTAC TGGGTCACTCCCATTGGAAATAAGATAACTGTGGAAACCCTTTCAG ATAAATACAAGCTAAGTAGCGAAGGTACCTTGGAAATATCTAACAT ACAAATTGAAGACTCAGGAAGATACACATGTGTTGCCCAGAATGTC CAAGGGGCAGACACTCGGGTGGCAACAATTAAGGTTAATGGGACC CTTCTGGATGGTACCCAGGTGCTAAAAATATACGTCAAGCAGACAG AATCCCATTCCATCTTAGTGTCCTGGAAAGTTAATTCCAATGTCATG ACGTCAAACTTAAAATGGTCGTCTGCCACCATGAAGATTGATAACC CTCACATAACATATACTGCCAGGGTCCCAGTCGATGTCCATGAATA 500 CODING AGGACCAACTTCTCAGCCGAATAGCTCCAAGCAAACTGTCCTGTCT TGGCAAGCTGCAATCGATGCTGCTAGACAGGCCAAGGCTGCC 501 CODING TCTCCCAAAGAAAACGTCAGCAATACGCCAAGAGCAAA 502 CODING AACAGCCGACCTGCCCGCGCCCTTTTCTGTTTATCACTCAATAACCC CATCCGAAGAGCCTGCATTAGTATAGTGGAA 503 CODING GGCCTTAGCTATTTACATCCCATTC 504 CODING GCGGGAACCACTCAAGCGGCAAATCTGGAGGCTTTGATGTCAAAG CCCTCCGTGCCTTTCGAGTGTTGCGACCACTTCGACTAGTGTCAGG AGTGC 505 CODING TCAGGGAATGGACGCCAGTGTACTGCCAATGGCACGGAATGTAGG AGTGGCTGGGTTGGCCCGAACGGAGGCATCACCAACTTTGATAACT TTGCCTTTGCCATGCTTACTGTGTTTCAGTGCATCACC 506 CODING TGATGCTATGGGATTTGAATTGCCCTGGGTGTATTTTGTCAGTCTCG TCATCTTTGGGTCATTTTTCGTACTAAATCTTGTACTTGGTGTATTG AGCGG 507 CODING GTGTATTTTGTTAGTCTGATCATCCTTGGCTCATTTTTCGTCCTTAAC CTG 508 CODING ACAGTGGCCGACTTGCTTAAAGAGGATAAGAAGAAAAAGAAGTTT TGCTGCTTTCGGCAACGCAGGGCTAAAGATCA 509 CODING TGGCGTCGCTGGAACCGATTCAATCGCAGAAGATGTAGGGCCGCC GTGAAGTCTGTCACGTTTTACTGGCTGGTTATCGTCCTGGTGTTTCT GA 510 CODING TTGGCTCTGTTCACCTGCGAGATGCTGGTAAAAATGTACAGCTTGG GCCTCCAAGCATATTTCGTCTCTCTTTTCAACCGGTTTGATTGCTTC GTGGTGTGTGGTGGAATCACTGAGACGATCTTGGTGGAACTGGAAA TCATGTCTCCCCTGGGGATCTCTGTGTTTCGGTGTGTGCGCCTCTTA AGAATCT 511 CODING GTGGCATCCTTATTAAACTCCATGAAGTCCATCGCTTCGCTGTTGCT TCTGCTTTTTCTCTTCATTATCATCTTTTCCTTGCTTGGGATGCAGCT GTTTGGCGGCAAGTTTAATTTTGATGAAACGCAAACCAAGCGGAGC ACCTTT 512 CODING GCGAAGACTGGAATGCTGTGATGTACGATGGCATCATGGCTTACGG GGGCCCATCCTCTTCAGGAATGATC 513 CODING ATATTCTACTGAATGTCTTCTTGGCCATCGCTGTA 514 CODING GGCTGATGCTGAAAGTCTGAACACT 515 CODING CAGAAGTCAACCAGATAGCCAACAGTGAC 516 CODING CCCGTCCTCGAAGGATCTCGGAGTTGAACATGAAGGAAAAAATTG CCCCCATCCCTGAAGGGAGCGCTTTCTTCATTCTTAGCAA 517 CODING ATCCGCGTAGGCTGCCACAAGCTCATCAACCACCACATCTTCACCA ACCTCATCCTTGTCTTCATCATGCTGAGCAGCGCTGCCCTGGCCGCA GAGGACCCCATCCGCAGCCACTCCTTCCGGAACACG 518 CODING GGGTTACTTTGACTATGCCTTCACAGCCATCTTTACTGTTGAGATCC TGTTGAAG 519 CODING TTGGAGCTTTCCTCCACAAAGGGGCCTTCTGCA 520 CODING AGATTCTGAGGGTCTTAAGGGTCCTGCGTCCCCTCAGGGCCATCA 521 CODING CACGTGGTCCAGTGCGTCTTCGTGGCCATCCGGACCATCGGCAACA TCATGATCGTCACCACCCTCCTG 522 CODING GGGAAGTTCTATCGCTGTACGGATGAAGCCAAAA 523 CODING TTGACAGTCCTGTGGTCCGTGAACGGATCTGGCAAAACAGTGATTT CAACTTCGACAACGTCCTCTCTGCTATGATGGCGCTCTTCACAGTCT C 524 CODING TGGAGAGAACATCGGCCCAATCTACAACCACCGCGTGGAGATCTCC ATCTTCTTCATCATCTACATCATCATTGTAGCTTTCTTCATGATGAA CATCTTTGTGGGCTTTGTCATCGTTACATTTC 525 CODING CAGTGTGTTGAATACGCCTTGAAAGCACGTCCCTTGCGGAGATACA TCCCCAAAAACCCCTACCAGTACAAGTTCTGGTACGTGGTGAACTC TTCGCC 526 CODING CACTACGAGCAGTCCAAGATGTTCAATGATGCCATGGACATTCTGA ACATGGTCTTCACCGGGGTGTTCACCGTCGAGATGGTTTTGAAAGT C 527 CODING GGAACACGTTTGACTCCCTCATCGTAATCGGCAGCATTATAGACGT GGCCCTCAG 528 CODING CTATTTCACTGATGCATGGAACACTTTTGATGCCTTAATTGTTGTTG GTAGCGTCGTTGATATTGCTATAACTGAA 529 CODING GTCCCTGTCCCAACTGCTACACCTGGG 530 CODING AAGAGAGCAATAGAATCTCCATCACCTTTTTCCGTCTTTTCCGAGTG ATGCGATTGGTGAAGCTTCTCAGCAGGGGGGAAGGCATCCGGACA TTGCTGTGGA 531 CODING GCGCTCCCGTATGTGGCCCTCCTCATAGCCATGCTGTTCT 532 CODING GTTGCCATGAGAGATAACAACCAGATCAATAGGAACAATAACTTC CAGACGTTTCCCCAGGCGGTGCTGCT 533 CODING TGAGTCAGATTACAACCCCGGGGAGGAGTATACATGTGGGAGCAA CTTTGCCATTGTCTATTTCATCAGTTTTTACATGCTCTGTGCATT 534 CODING ATCATCAATCTGTTTGTGGCTGTCATCATGGATAATTTCGACTATCT GACCCGGGACTGGTCTATTTTGGGGCCTCACCATTTAGATGAATTC A 535 CODING AACACCTTGATGTGGTCACTCTGCTTCGACGCATCCAGCCTCCCCTG GGGTTTGGGAAGTTATGTCCACACAGGGTAGCGTGCA 536 CODING CATGTTTAATGCAACCCTGTTTGCTTTGGTTCGAACGGCTCTTAAGA TCAAGACCGAAG 537 CODING ATTACTTGACCAAGTTGTCCCTCCAGCT 538 CODING CGTGGGGAAGTTCTATGCCACTTTCCTGATACAGGACTACTTTAGG AAATTCAAGAAACGGAAAGAACAAGGACTGGTGGGAAAGTACCCT GCGAAGAACACCACAATTGCCCTA 539 CODING TGCTTGAACGGATGCTTTAGAATTTTCTGCCTGAGCTACGGCACCA AGCTGGTTAGTCGGAAGGCGTTTGTGGCTAAGGCCTTGAAA 540 CODING GCGGGATTAAGGACACTGCATGACATTGGGCCAGAAATCCGGCGT GCTATATCGTGTGATTTGCAA 541 CODING TGGTGCCCTGCTTGGAAACCATGTCAATCATGTTAATAGTGATAGG AGAGATTCCCTTCAGCAGACCAATACCACCCACCGTCCCCTGCATG TCCAAAGGCCTTCAATTCCACCTGCAAGTGATACTGAGAAACCGCT GTTTCCTCCAGCAGGAAATTCGGTGTGTCATAACCATCATAACCAT AATTCCATAGGAAAGCAAGTTCCCACCTCAACAAATGCCAATCTCA ATAATGCCAATATGTCCAAAGCTGCCCATG 542 CODING GCTCCCAACTATTTGCCGGGAAGACCCAGAGATACATGGCTATTTC AGGGACCCCCACTGCTTGGGGGAGCAGGAGTATTTCAGTAGTGAG GAATGCTACGAGGATGACAGCTCGCCC 543 CODING GGCTACTACAGCAGATACCCAGGCAGAAACATCGACTCTGAGAGG CCCCGAGGCTACCATCATCCCCAAGGATTCTTGGAGGACGATGACT CGCCCGTTTGCTATGATTCACGGAGATCTC 544 CODING ATCCGAAGGCTTGGGACGCTATGCAAGGGACCCAAAATTTGTGTCA GCAACAAAACACGAAATCGCTGATGCCTGTGACCTCACCATCGACG AGATGGAGAGTGCAGCCAGCACCCTGCTTAATGGGAACGTGCGTC CCCGAGCCAACGGGGATGTGGGCCCCCTCTCACACCGGCAGGACT ATGAGCTACA 545 CODING GATGTGGTCCATGTGATGCTCAATGGATCCCGCAGTAAAATCTTTG AC 546 CODING TGGGAGTGTGGAAGTCCATAATTTGCAACCAGAGAAGGTTCAGAC ACTAGAGGCCTGGGTGATACATGGTGGAAG 547 CODING CCTGAGGATTCATCTTGCACATCTGAGATC 548 CODING GGTGCTGGACAAGTGTCAAGAGGTCATC 549 CODING AGAAGGTTCTGGACAAGTGTCAAGAGGTCATC 550 CODING TTAGTTGAAAAATGGAGAGATCAGCTTAGTAAAAGA 551 CODING GTCACAACGGTGGTGGATGTAAAAGAGATCTTCAAGTCCTCATCAC CCATCCCTCGAACTCAAGTCCCGCTCATTACAAATTCTTCTTGCCAG TGTCCACACATCCTGCCCCATCAAGATGTTCTCATCATGTGTTACGA GTGGCGCTCA 552 CODING CGGTGCAAGTGTAAAAAGGTGAAGCCAACTTTGGCAACGTATCTCA GCAAAAAC 553 CODING CAGGAAAGGCCTCTTGATGTTGACTGTAAACGCCTAAGCCC 554 CODING ATGTTAAGTGGATAGACATCACACCAG 555 CODING GCGCATCCCTATGTGCCGGCACATGCCCTGGAACATCACGCGGATG CCCAACCACCTGCACCACAGCACGCAGGAGAACGCCATCCTGGCC ATCGAGCAGTACGAGGAGCTGGTGGACGTGAACTGCAGCGCCGTG CTGCGCTTCTTCCTCTGTGCCATGTACGCGCCCATTTGCACCCTGGA GTTCCTGCACGACCCTATCAAG 556 CODING ATGGTTTGGGCCACTTCCAATCGGATAG 557 CODING GGATTGGAGAAGCACCATATAAAGTAGGGGTACCATGTTCATCTTG TCCTCCAAGTTATGGGGGATCTTGTACTGACAATCTGTGTTTTCCAG GAGTTACGTCAA 558 CODING ACTTGGAGGTGGACCATTTCATGCACTGCAACATCTCCAGTCACAG TGCGGATCTCCCCGTGAACGATGACTGGTCCCACCCGGGGATCCTC TATGTCATCCCTGCAGTTTATGGGGTTATCATTCTGATAGGCCTCAT TGGCAACATCACTTTGATCAAGATCTTCTGTACAGTCAAGTCCATG CGAAACGTTCCAAACCTGTTCATTTCCAGTCTGGCTTTGGGAGACC TGCTCCTCCTAATAACGTGTG 559 CODING ATCCCGGAAGCGACTTGCCAAGACAGTGCTGGTGTTTGTGGGCCTG TTCGCCTTCTGCTGGCTCCCCAATCATGTCATCTACCTGTACCGCTC CTACCACTACTCTGAGGTGGACACCTCCATGCTCCACTTTGTCACCA GCATCTGTGCCCGCCTCCTGGCCTTCACCAACTCCTGCGTGAACCCC TTTGCCCTCTACCTGCTGAGCAAGAGTTTCAGGAAACAGTTCAACA CTCAGCTGCTCTGTTGCCAGCCTGGCCTGATCATCCGGTCTCACAGC ACTGGAAGGAGTACAACCTGCATGACCTCCCTCAAGAGTACCAACC CCTCCGTGGCCACCTTTAGCCTCATCAATGG 560 NON_CODING TAGTCTTGGCTCGACATGAGGATGGGGGTTTGGGACCAGTTCTGAG (INTERGENIC) TGAGAATCAGACTTGCCCCAAGTTGCCATTAGCTCCCCCTGCAGAA TGTCTTCAGAATCGGGGCCCG 561 NON_CODING GAGCTTACCTTGAACCTTTGAATTGGGCCAAATTGCGATGACCACT (INTRONIC) GCATCCTGGAAAATTTTATTTCACCAGCACTACAACTCCTCAACAG CACCAACCAATAAACTATGGATTTTTGTACTAAGCCAGTTGCCTCTT TCAAAACAACTTGTCAACTTGTCTAATCACCCTCAGCTTTTTTTAAA AACCCCTCCTCTACCCTCTCTCTTCAGAACACAAGTGGCTTCTAGCT GAATCT 562 NON_CODING GATGCTTGACATCCCTAACTAGACAGATGAGGGTTGAAGTTAGTTT (INTRONIC) TTGGTGGGGTTGGAGGTGAACATCAACTACCTTCCTAGTTCCAGGT AATATAGAACATGGAGTGAAGTGTAGATAAATGGGTCTGGTGGGT CCCGAGGTCATCTTATCACATAATGACTAATTTACATTATGGAACC CAGTACAAAGTGTTCCAGTTAG 563 NON_CODING TAAAGCCACAAGTCACCCTTTGCTGAAGTCAGTATTAGTAGTTGGA (INTRONIC) AGCAGTGTGTTATTCTTGACCCCATGAAGTGGCACTTATTAAGTAG CTTGCTTTTCCATAATTATGGCCTAGCTTTTTAAAACCTACTATGAA CACCACAAGCATAGAGTTTTCCAAAAG 564 NON_CODING TGGAGAACAACATTGGGGCCCTTGACTTTAGATTTCAGTGGGGACC (INTRONIC) TACAAAAAGGAAAAATGGAAAGGGAATTCTGAAGTCTTAAGGTGG GCTATCTGAAAGTTGGATCCCTGGGTGAAAAAGATTTTATAATATT AGATGAGTTGAGAGAACCAATGTGAATTAAAGCTGACTGGCTTAA AAAAAATAAACCCATCAAAATTAGTAAGGGAATAATGTTATTCATT GCCTTTTTTTCGTTGAGTTATGAAAGCTCTTCGAAGATGAAGGTTTT ATGAAACTCAAGATCTCTCCAGAGGCCGGGCACAGTGGCTCACGCC TGTAATTCCAGCACTTTGGGAGGCTGAGGTGAGCAGATTGCGAGTC CAGAAGTGA 565 NON_CODING TGTGCAGCCGAAGAATGAGTGTAACATGATCCTTGCAACAGAAGA (INTRONIC) AAAGGACACGGAGAGGTCATTTGGTAGGAGGCTCCACTGTGAGAT GACCACCGATGATTACTTCTGCCGAAAACCTAGCAGTCACAGCA 566 NON_CODING TTTGGGATTGGTTTAGAGGCAGCTGAACGAAACTTATTTTTCATCTG (INTRONIC) TAGTAAATACCTTTCATTTAATGTGAATGGTAAAATCAAAGGGCAG ACGCTG 567 CTTGCCTGTGGCACCAGATGCCTTACAGTGGCCAGGAATGCTGCGG NON_CODING GACAGTCTACTTTGATTGCTTTCTTTCCTCCATGGCTGAGATCTGAG (INTRONIC) TGTAGTGTTAACTGGGCTTAAAAATCAAGTCCGTTGTATCTGCATG GTCACGTAGTTCGGCATCTCATGGCTTTTGCACCTAGA 568 TGAATGACCATACAAGGACTCCATGGTATATTCTTGTAGATCATTA NON_CODING GTTAATTATCAACAATTGGCTAATGATTAATGTTTGCCTGAGAGGC (INTRONIC) TGACTTTTTGTCCATTAGTAATGACATCCCAGGAAACACCTGGCAG AGTTCGTCTTTAATTTC 569 AGAGAGCCTCAAAATGACCAGAGTAGATGGACTCGTGTAGTAAAA NON_CODING CTTTACCCAAAGTTGGTTTCCTAATGATATAATGTGAAACAGTCTAT (INTRONIC) GTGCTATACAAATAATTATATCTCTTTTGTTAAGCCTTACGTCATTT TGACAAAGGCTTTACTTGATTGAGTATTGACGGCTTTTCCA 570 NON_CODING TTGGGGAAGAAGAATATCCAATCCG (INTRONIC) 571 NON_CODING AGTGCAATGTGTCATGGGCTCTGAAGGTCTTACGTTGAGGAATGGC (INTRONIC) AATATTATCAGAATTACGTGTCCAGCTTCCCAAGCTTACTACTTTGA 572 NON_CODING CCCATTTTGAGGGACTGCCAAGCTGCTTGCCAAAGCAGCTGCGCCA (INTRONIC) TTTTACATTACCACCAGCAACATGTGGAGGTTCCAATTTCTGTACGT CTTTGCTAACACTTGTTATTGTCTATCTTTTTAATTATAGCCATCATA GTGCATATGAAGTGGTATCTCATTGTAGTTTTGATTTGCATTTCTCT GATGACTAATAATAGTGAGCATCTTTTCATGTGCTTATTAGCCGTTT GTATCAAATCCTTTGCTCATTTTTAAATTGAATTTTTAAAATTATTG GTTTGTGGCAGGGCATGGTGGCTCATGCCTGTAATCCCAGCACTTT GGGAGGCCAAGGCGGGTCGGTCACCTGAGGCCAGGAGTTCGAGAC CAGCCTGGCCAGCATGGTGAAACCCTGTCTCTACTAAAAATACAAA AAATAGTCAGACATGGTCACAGGCA 573 NON_CODING TCTGGACTTTCACCTTGGGACATTCTCAGTTTCCACCCCACTGTTTC (INTRONIC) TGAGGGTCGAAAGGTTTGGGTGTATATGTAGGGAAAGATAATTGGT AGGCTCTGAAGCACACAGTTCATTTGTTTTTCAATAAGGAAGAGTC ATGTTAGAAATTTTGTCCTTTCTTCCAGAAGGTACACTATATAGCCT GGAGCCACA 574 NON_CODING TCCAAAGACAAGCTTAATGACTGCTGTGCCAACACACAAAACTACA (INTRONIC) AGATACATTTAAGCA 575 NON_CODING GTACCTCTCCAGATTAGACAAGATGATATTAAATATTTCCATCTTAC (INTRONIC) AGATGAGCAAATTCAGACTTAGAGACGATAAGGTACTAGCCCCCT GGAAAACAACTGCACTGAACCTAGGTCCTTTATTTCTGAACAAGAC AGGCATCGTGTTGAACTTCATG 576 NON_CODING TAGCCATTCTGCACTCTTCAGGAGAGAAGAACAACCTGGGGCCATG (INTRONIC) TGTTCAATAAAGAGATGGGGCTGGCACATTGTTGAGGAGGAGAAG GAGGATTTCAAATGGAGGGCTTTTTGAAGAAGGCATTGAACACCTC CCCACCCACCCCTGCCCTGCACTTCTCCCTGTAGCTCAGAAACCTTT TAATAGCCATGGGACCAACATCTAGCAGCTGGCTTGGTTTTGCTGG TCCTTGCTTTAAAATGGGGATACATATCCCTGCTTTACAGACCTGCT GTGG 577 NON_CODING AGTTACGATTAATGTGAGCAGCTTCTCTCATTCCAGAAATGTGACC (INTRONIC) TCTGGTTACAGCAAATGTGACAACATGAATTACCTTCAAT 578 NON_CODING GAAGCAACCCATATATCCCTCAACGGGCGAATGGATAAACTCATTG (INTRONIC) TGATGTATTTGTGTAATGGGATATTACAGAACAACAAAAAGAAATG AACTGCTGATAAAACAACGTGGATGAGTGTCAGAAACATTATG 579 NON_CODING GTGGGTTTCAGAATCACTGGTGCTTTGAG (INTRONIC) 580 NON_CODING AAGTACCCTGGGGAGAGAGTTTATGGAGTGTTCTTTGCTTGGATAA (INTRONIC) 581 NON_CODING GGTGGGTCCAATATGTAGAAAGGCACACTTAGAACAGGACTATTTG (INTRONIC) GATGTGTGGGAAGTGGGATCATTAAGTTCTGGTGGAAAGAAACCT ATGGTAGAGTTCTTTGATAAA 582 NON_CODING GCAGGAGTTTTGTCCTCTACCAAGACCTTTCCTGAAAATCACTTATC (INTRONIC) AAGACAGTTTCCTGTAAGAAAAAGCCATATCCCAGCTGATTTTCCT TCCTGGGGCCAAAATCTGCTATTATTCGGCCTGAAAGCCTTGATGA CTCTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG TGTGTGTGTGTGTGTGTATGGATGCTTGTGTGTGTGTATGGGGAAT ATGTGATTAATGTGTGTTGGCTGCTGTTGTCTCTGATTTGGCTA 583 NON_CODING TCCTGAGGACAGTTGCCAAGACCACACAAGCTTTGCTGGATGAGGG (INTRONIC) CCGCCAAGAGGGGTTGCCAGACATTTTATGTGTCCTCTGAGATGCT TTCTTTTCTGCTGAGGCTTCCCAAATCAAGCTGTTTCCTGGAACCTC ACCAGGCTTCATGAAGGAGAACTATAGAACGATTATTGACCAGAA ATTAATCAGCATTGTTGCTTGAGATTTAAACAATTTCCATAGCATGC CCTTTTTTTGTCTGTTCTAAAGTGAGATACATTTATAATTGCTTTATT TGTCTGGATCCAAATATAATGCAGATTAATTGTTATAAAACGATAG CAAAATGAGCTGGATTGGGTGGGCTTTTGGTAGTCCCCATTTGTAG ATTTCAGCCGCTGAGCTTGTCCTTATT 584 NON_CODING CTCCTAGTAAACCTCAGTGGCCTTAGGCTAGGGTTGGACATGTGAG (INTRONIC) GGTGGTGTCTATTCCTGGAGAAATAACATCGCATTTGATTTTGCCA CAGGAGCTTTCTATACAAGGTTAACAGCAATCCTGTTGTGAATTCC TTGGCGCCTCATGTCTCCTAAACCCAGCTAAACTGACGGAGGCCAT G 585 NON_CODING CTGAGATCCTGTAGAGTGCCCGGCTCTGGTCCAGAGGCGAGGGGTG (INTRONIC) CCAGGATGTCTCAGACACAGACAGCGGCCTTGTGCTTAGGCGTTCA TTATCTCATGGGGTAGCCCATTTTGAAGCAGTGCAGAAGGGCACAT ATTCAGTAGAGGTGCAGACCCAGAGGCTCTGTGAGCTGCACTAGA GAGATGAGGAGGCATCTCCCCCGGCGACTGACGATGGGCTGGCAT GCCTCCACCTCCGCCCCTCCGCCCCCTCGCCCTCCCAACCACCACCT TCCCTCTCTGCCTGCTACTCCCCTCTTACTTTCCCATTGATATTTTTG TTGTTGTTTAAGCAAATTATTATTATTTTTTTAAATTTTAGCCTCAA GAGTCTTCATAATTTTTTAAGGGAACACTAGAGGTACTGC 586 NON_CODING GGTGCAGGGTACTCTTTGGAAATTCTGGAGTGTAGCATTTTCTGGA (INTRONIC) TTTCCCAGCAGGTGGCCACACTTTACACACACATCAACGTTGTACT CAATGTCACCCAAGAGGTGGCTCTGGAGAATGTGGAAGCACTGTGT CAGCTGCAAAGTATTACGC 587 NON_CODING TGTGCTGAGTTGACTTCTCTGTCCGCAGTTCCCCCTCCACCTGTGCT (INTRONIC) CTGGGTTGTTGATGTGCAGGTTAGAAGAGGGAGGTTGTTGAGGGTA TTAGTGTTGCAGGGGAGGCTGTT 588 NON_CODING GCACCGTGTAGGCACTGCAGTGACAGTGTGGAATGAAATGGTTTCT (INTRONIC) TTCTTCGTGAAGCTTATATCTAATGATGGAGGCCAAAATGACAATT ACAAACTCGTATAAATGCTTTGAAAGAAAGGTTCATGTGCTGTGAG AGGGTTTAACAGGCACAACTGCTGTCAGTTTATTGGGTAGGAGCAT CCTGGAAGTGAAGAATGAGTAGTCCACATATCCAGGCAAGGTGGG ACAAGAAGCTAGGGCAAGGGTATTCTAGTCAAGGGAAAACCCACA GAAAGGAGGTACAGTAGGAAGGAGCAGAGGATGCTGGAGGAACT GAATGAAGCTAGGGTGACAGGAACTGGGAGAGCTGGAGATGAAGT CAGATGAAAGGAAAGAGACTGGCCGGCAGAATCCAGGTCACGTAG GACCTTTAGACTATGTC 589 NON_CODING GAAAAGGTAGCAGGTGTTAATTATGGAATCTAGGTGAGGTAGGCA (INTRONIC) TATGGGTGTTC 590 NON_CODING TGGTAGACTGAGAACTTAAGGATGCATATGATAATCTCCAGAGTAA (INTRONIC) TGACTTAAAAGGGGTACTAAAAAGCTAAAAGAAGAGATAAAATGG AATATTAAATAGTACTAAATTATCCAAAATAAGTCAGAAAAGGAA GAAAAAGGAACAAAGAACATATAGTACCAACAACGAGATGGTAGA CAAACCCAGTAATATC 591 NON_CODING CGTGGAACATTCACCGACATAGACCATATCTTGGCCATGAAAGTCT (INTRONIC) CATTACCTCTCGATTGAAATTTTACAAAGTATCTTTGTTCTAATGGC AGTAGATTTAAAACAGAAGCCAATAACAGGCTGTTTATAAACCTTC CCAAATGTTTGGAAATTAAATAACCTATAACTCAAAAAATAATAAA AATTAGAAAATACTTTGAAACTGATAAAATCCAACTGGGAAATTGT ATGATCCGTTGAATGCAGTGCTTGGAGGGACATTTATAGCTATATC 592 NON_CODING ATGGCAGAGACTCAGGCTGTTTTGCCAAAACCCAGGTCGCTTTCCC (INTRONIC) CAGCTGTGCAGGCTCGTATTCTGCTGAAGCTGCTGTTGGTTATTCCT GGGACCCTGG 593 NON_CODING CAGATGGGGTGTCACGGGGCCCTGACAAGGAAGGTCCACATGAGG (INTRONIC) GGAGATGATTACACTGGTGTGCTAGACCCAGGGGA 594 NON_CODING TTCCTGCATGCCTATATGAAGTGGCGCCAAGGGGAAATAGAGACAT (INTRONIC) GGGAAGAAATACATGAGAAATGGACAGACAACATTGTCCGTTCCT GCCTGCAAGG 595 NON_CODING CACGTCCCATATGGTGGATATAGGAACTGCATATGTGTGCAAGTGT (INTRONIC) AGTTTTGCATCTGCACGTGAATCTATGAATATCTAGATTTTCTAACC CACTTAAGGGCTGCATATG 596 NON_CODING GGCCATGTTTGGAAAGCTACCTAGTGAAGAGTCCTTCCCCAGTCTG (INTRONIC) GTGTCCTCTAGGGGTGTCCAGCATAGCGTAGCCCACTTGCGTTCCA GCTCCACCAGTTCCCTTCATGTTGAAACCTCCTCCATCCCTTGTAGG GGAGATGGGGATGGAGTCTAATCGCTCTCTCTTCATCCGTGTACTG TTCCCTCGTCAACCCAGAAAGAACCCACTGTTCAGCCACAGCAGCC TGAGTGGGCTTTTCTAGTGACCCCACTCTGTATGGCCGCTCGAGAT CTAAAGGGCATTAGCTGGTATAGGCCACCTGTTAACTACTCGGGCC AGCTTTA 597 NON_CODING GTGCTGTGTGGACGCAGTTTTCCGAGCTCTGTGTTGTTAGCATGTAA (INTRONIC) CTCT 598 NON_CODING TGCATGTTCTACTTTCCATTGGGTTTGACCTCTCCATGATAACCC (INTRONIC) 599 NON_CODING TAAGAGCCATGCCAAGGACTTCTCTCTTTGTCT (INTRONIC) 600 NON_CODING GTAGACGTGTTGGTCACATGTGATGAG (INTRONIC) 601 NON_CODING GGCAGACTGCGTGCTAATGGAAAGTGGAGCATGGCCGTCGCAGTG (INTRONIC) TGAGCGCAGAAGTGCGGACCTAGGC 602 NON_CODING GAGTTCCTTTTGTATGCCAGTCCGCCATGACCTCCTGAGCGTCCGGC (INTRONIC) CCTGCTCTCTGCAGAGACCCAGTCCAGAATACAGTGAGAAGTGGAC AGGCCAGGAAGCTCAGATACACCCATTGAAACTAACACATACACC CGCATGCCAAAACCAATCCAGGCAACACCTCAGGTTCCATCTTAAC GTGTCCACAGGAAACACCACCACACCCAAACCTCATCTAACATTGT CCGTCTTTAATTCGTGCTCAGAGCCAGTCTGGGGATGCCTCTTTGGA AGCAGTGTGGTCTAGTTTCAAGGACACTGGGAGTCAGGGAACCTG GGTTCTAGTCCCAGTTTCAGCATTCACTTGCTGCGTGACCTTGGGCA AGACACTTAACCTCTCTGTGCCTCAGTTTCCCCCATCTGTAAAATGG GGTTAATAATGTCGACCTACCTCACAGGGCTGTTGTGAGGAATAGC TAAGTGATTGTAAAGCACTTTGAACGTATAATTGCTTATTAAGACT ACAACAATAATAATATCATATGCCTGTTTACTACCAGAACTTTAAG AAATTCTTGTTTTCCTTTGATCTCTTTTCTGTTCTGTACCATACTTAC CCATTGAGAAGGAAAATTCCCCCCTTTTAAAGAAATCTAGGCAATG CACAAAGATGTCAACAGAGGTAACCCTGCAGGTTGCATTTTCACAT CTTAAGAATAGCAGATTTTTGCCCAAGATGTTGGTCGATAAGGGTG TCTGATCTTGAATTCTCAGCTGATTCCAAGTGGTGGTTGGAGTCTGT ACATCTGATGCTGAGCCCAAGACACCCAAAGTG 603 NON_CODING TCATAGGCCCTTGAGACCGTGTGGATATAGTGAACCCAACTCTTGG (INTRONIC) TAGACTTG 604 NON_CODING TTCTGGACTTAACACTCCTCAGCTGTAAAATGAGGTAGGAAATCTG (INTRONIC) ATGTGATTTCTAGTTGGGGACATTCTAGAAGATTCCATATTGTATCT CAAATGACTGTTCAGAGACACAGTCTTTAGGTGCTCACTCTAGAGA GGACTGTGATAAGC 605 NON_CODING ACAGAAGTGGTGTGCAGATCGTTTCAGATCAATTTATCATAAAATC (INTRONIC) TAAGTTGATAGGTGTTCTCTTAATGATGTTCTTATACTGCCTGTTCA CCTTGACCCTTTAGCTTTGAGTAGATTAGAGAGTGTAGGGGAAAGA TCTTTTTCCCTTCAAATACTCAAAGGATCATGTGTTCTCTTGAGCAG TTCTGCAAATCCATATAGGA 606 NON_CODING TTCATGAACTGTCGGCCTTCCTGTGTAAGTGGGTCAGGCACCATGT (INTRONIC) GACCTGCTCACTGCCAGTTTCTTCTTTGAATAGATGTTTATTTCATG GATCATTTTGAAGATTCTCCGTGGGTGTGCAACATGGTTTTAGAAT GTTGGGTAATTTCTCATGTGTTCTTTGAGATGGATGGCTTCTCAGTC GTCTTTGCAGTCAGCCACTGTAGACTTGAGTTTCTCTCTTGCTGTCT TCATTTTATTGCTCCATATCTGAGGAAAACCATGTGAAAAATCCCT AGACACATAGGAGCCCTGAGAAGTGGTGGCAGGGAATGCTTGGGG GACAAAACAGATTTTAGAGTTACGGGTATTTTAATTAAAAAAAGAG AGACCCAGAATTGTTTTTCACTTAAATGAGCAATTATATCTTTAACT TGGGGATGGAAATATGTTGTGAAATTTGTTTAGTCAGCTCCCTCTG AAATAAATAAAATTACAGTGATGATATCATTCTTGTTTAAAATGTT TGAAAAGGTATCAAGACAAAGTGATTAAGGCCTAACTGTTTGCCAA ATTTTCTTTAAAGCTCCATTTTTGGGGTATTTCTATGCCAAAAAACA TCTTAAACTGATGAACATATAGTTCTCCGCACTTGTATTGGCTGGTT TTTA 607 NON_CODING TCCACTGGATATAGCCTCGACTGTACTCACCAGGTTCTCCACACCCT (INTRONIC) AAGCCACATGCCAGATTTGTTTAGCAGATTCAGTGGAGCAGGTTCA TTCATGGGGGCACCAAACCAAAAGTCCTTTTAAAAACAGTTACCTA TGATTTAAAAGTGTGAAGTGATTGTAGTATGATGGGGAAACAGTGG GCCAACTATCATGAGAATTAGGAGATCTGGACAGCTACATGATCTC TTTGATCATATAGTTTTCTTACTTGCTCAGTGCAGCAGTAGTGCCAA CCTGTCCTCAGACGGGGATGTAATA 608 NON_CODING GGTTGTGGACCACTGAGCTAATGCAGTGCATCTCAGTGATTACTGT (INTRONIC) CCATCAGAAGCTTGTTAAAAAATATTCTTGAGCACCACCCCCAAAG GTTCTGGTTCAGTAGGTCAAGGGTGGGGCCCAAGAATTTGATTTCT ATAATGCTTTTAAGTGAAGCCAATACAGACCACACTTAGAGTAACA TGTTCTAATTTTTTTATGAACCAGGAATTAATAAACTGGGCAGATA GTAAAGCATTGCCCACAGAGGTTGAAAGAGACTTTCAGATTCATCG AGTCTAATCTCATCAGATGGTTGAGCTTCTTCCACAAAATCCCCAC CAAGTGGGTCTCTTTGAATGCTCTACCAACAAGGATCC 609 NON_CODING TCCAGGCCTTTTAATGAACAGTCTTCTGCTTTTTCTCTTAACAATAT (INTRONIC) AATTTCTCCTATGGAACAATTTGAAAGCCATGCATGCAAATTTAGA CTAAAAGCAATGGACACAAAAGAAACCTGTATACATTCTTCGGTAT TACGCACATGTGATGAGTGGTGCTTTTGGGCACTTGCCTGACAGTA GCTTGGACAGAAAAGACACTGGAGCCTCAGAGAATAACTATTGAA GCAATTCTGGAATTAAGAAATAAGGCCTGAAATGAGATGGTAAAA GATGTTAGAGGAAGAGAAGCAAGGTAAGACAAGGTGACACACAG AATCAGAAATGATGAACAGGAAGCAACTTTTAAAATAAATGTTTTC TGAGTAGCTACTAATATGCCAAGCCCTGTGCTGGGCATTGACATTG CAGCAGTGAACAAAACAGACACGATCCTGGCTCTCGTCAAGTTTAT ATTT 610 NON_CODING GATGGAAAGTAAGGGCAACAAAATAAACTTGAGAGCCACAAACCT (INTRONIC) GTGGGTTACAGTTAAAATTATAAAACACTGTCAAAATTTAATTAAT TTTAGGAAGTTCACTTTGTCCTCACAACAGGTTTTTGAAGTATATTT TTCTAAGTATTTAATACGTACTCTTAACAGTCTGCAAATTTGCAAAA CCTGAAGTTAATGAGTGGTTAATTGACTTAAGATTTTTTCCAGAATC AAATTCCTTTCTCCATACATACATGCGTTG 611 NON_CODING TGAGGGCCAAGACACAAGATGAAGCTTTGGCTTCTTAAAAAGATG (INTRONIC) GGACGAATGCATCTGTCAGTGGCTGGTTACAGCAATGGGTTAGAAT ATTTAATGAGGGAGGTCATCACTCCTGCTTCCCTT 612 NON_CODING GGGTCACAAGCCAATAGACAAGCCAGTCCTTTTGAATCCTTTACTC (INTRONIC) ATGGCCTTGAGAGGAACCA 613 NON_CODING GGGCTGGGATTATTGTCTTCATATACAAAGGATAGTCTTTTTTTTTT (INTRONIC) GTTTCTATTTTGCAAAGTACCCATTTTCAGCACAATACAAAAGGTA GATATAATGCTGTGTACTTTTTAAAATAATCTTTTGAATATTATACA TTCATACTGTCCAAAAATTAGAAAATATAAAAAGGAATACAGTGG AAGCCTCCATGACCCCACAGGTAACCACTAGCATTATTTTCTAGTA GTCTTTTATGTGTTTATTTTATGCAGTCTTTTATGTATTTTATGTAGT ATTTTATGCAGTCTTCCAATTTCCTTATGCATATACAAACATAAAAA TATATTCTGATAGTTTCTTCTTTTGTTACACGAAAATGGTATACTAT TCATAGGGTTGGGCACCTTGGTTTTGTTTTGTTTTTTTTTTTCCATTT AAGAAAATATATTGGAAATATTTCTATATCTGTATGTAAAGAGTTT CCTCCTTTTCTTTCTTTTCCTTTTTTTTAACAAATGTGTAATATTTAT ATTTATGCCATAATTTATTTAACCAGCCCCTATTGATAGGAATATGG GTCATTTTTCAATCTTTCATTTTTACAAACAGCATGTATGAATAACT TGTGCATCTAAATAGTTTCACAAGAATACCTGTGGGATAATA 614 NON_CODING TCTAATCCCGGCCTTGGCTTTCTGGTGACCAACCCCCATCCTGAAGC (INTRONIC) TGGCCAGGGACTGCCAGCCATCAATCAATCATTAGCATGCAAAAA GACATACTTTGGAGACTCCAAGGATTTTAGGAATTCTATGGCAGAA AATGGAGATGAACACCAAATAGAAGGCCGGGCACAGTGGCTCACG CTTGTAATCCCAACACTTTGGGAGACCAAGGTGGGTGATCACCTGA GGTCAGGAGTTTGAGACCAGCCTGGCCAACTTAGTGAAACCCTGTC TCTACTAGAAACACAAAAAATTAGCCAGGCGTGGTGGCAGGCGCC TGTAATCCCAGCTACTCAGGAGGCTGAGGCAAGAGAATCACTTGA ACCCAGGAGGCGGAGGTTGCAGTGAGCCGAGATGGCGCCACTGCA TTCCAGCCTGGGCAACAAGAACGAAATTCCGTCTCAAAAAAAAAA AAAAAAGACCAAATATATATTTCACAATATCATAGATAATGAATGG CATTTTTAAAAAAAAGTTTGTCTATTAACTGCTTACCGTGTTCTTGC CATGTAGGTTCTG 615 NON_CODING ACAGGGGCGCATTTGCCTCACAAGGAACATTTGGCAATGTCGGGA (INTRONIC) GATATTCTGGGTTATACAAGTGGGAGATTAGGAATGCTACTGGCAT CTAGTGGGCAGAGGCCAGGATACTGTGAAACATCCTATAATGCAC AGGAGAGCTCCCTACAACAAACAATT 616 NON_CODING TGCTTTGCGATGCATTTGAAATACCGTTTGTGGCCAGATAAATTAC (INTRONIC) GATTGCTTTTCAAGGTTACATGGTGTTTC 617 NON_CODING GGTCCACAGAGAATAGTCCATGATCTGTACAAACATCCAGAGAGCT (INTRONIC) GCTTTCTCCCATGGCCTCCCACAGGTCTGACTGCCAGAGAGTAGAA GCAAGAGGGGTGAAAATAGAGGAGTACCTGCTGTGCTGTCATTTCA GGTCTGCTCTGGAGAAGAACATGGGCTAAGAATTATCTTTTATGAT CTGAAAAAGCTGTCTGAAGTTCCTTCCAAGCTTATCAGCCTCCTAA CCTGAGCTTTAACAAAACCCGGTATGGTAGAGTCCTAGTGTGCCAA TCCAGCTTTC 618 NON_CODING TGGAGCTGCGTTGAATGCAAACTTGAGGTGTTTCCCTTGAGGAATT (INTRONIC_ CTTGTCTTCAAACGTCTGCAGAGTAATGGACCATGTTACAACTTTCC ANTISENSE) TGTTC 619 NON_CODING GATGGCACTGATGCATTAGACCCTCAGCAGCCTGCAATTGCAAATC (INTRONIC_ TGCGAGGTTTCATTCGGCCCATAAAGCAAACATTTGAACTTACACA ANTISENSE) GAATGAGCACTTAAATACGGGTGCAATAA 620 NON_CODING TGTAGCCCATTTGGTCACAGTAGCCTCACTTCTGCTACGCTTGCAAC (INTRONIC_ AACAACTCTTTGGAAATCAACCGCTATTCTATATTTGTGTTCACGTT ANTISENSE) AGTG 621 NON_CODING GGGCCTAGGCTTTGTGCACACTGTTCGATGAAACCAAGGCTTACCA (INTRONIC_ AGCTCTACTTTATTCCGTATCTGGATGGTCATTTCATTTCTCCTAGC ANTISENSE) CCACACCCAGACACACACTTCTCAAATACACACGACAATTTCACTA TCTCACAATCTCTTACTGTAACTTTGGCCTTCAGAAACACCCTTTGT TATATTGCAGGCGGCCAAGCATTAAGTCCAGCTGA 622 NON_CODING ACCTGTGCCAGCTCCTGCAAATGCAAAGAGTACAAATGCACCTCCT (ncTRANSCRIPT) GC 623 NON_CODING CCATTGTATACCCTTCCTTGGTGAATGTTCTGATATTTGCTTCCCAT (ncTRANSCRIPT) CCCAAGTTGTTTCAGCCCCTATTAG 624 NON_CODING CGGATCCGTGTTGCACCTTCTCCTGCTGCCACGTGTGAGGCAACTCT (ncTRANSCRIPT) GCGTGTCTCCTAGCTGCTCCCTGACAGCTTCTCTGCATGTGTTTGGA CTCTGATGTCCTCTCAGTGTGTTGCTTTTGGATTGAACTGTGATTCT TTCTGCCTGTATCTGTCTGTGAGATTCCGTGTTTCCAATGC 625 NON_CODING GGAGATTTCAGATGGACCTAGAATGAGGAAGGCAGGCTACTCAAC (ncTRANSCRIPT) AGTTGTGGATTTGGGAGTCTGGACACTCCTTGAGCTGTGCAGTTTT AATTCTTTCTTAAATAAAGATACAAAGGACAATTTAGGACATGGAA AACCCTAGCTA 626 NON_CODING TGACCTCTGGGGTAGGTTACTATCCTCTTTGTCCTGCCAGTACCCCT (ncTRANSCRIPT) AGAAATTTGACTTAATTGCTGCATCTAGGGACTTAGGGATTTTTCCC AAATGCTGTGTAGAAAGTCACTGGAGTTAAATCTACTCCAACCATT TTTCTGCTGTTTCTTGAAAAGACAGGATGATTCATTTACATCTCTTT TCCTTCACAGAATCATGAGGGAAGTATTGTGATTACCAGTGTTAAG CATTTG 627 NON_CODING ACAGCTCCTCCTTCTTGATATTGCACATGCACTTCAGTTCATGGCTA (UTR) GCTGTATAGCTTCCGTCTGTAAACTTGTATTTTCAAGAATCCTTGGT ATTGAATTTTTAGAAATGCTCACATAATTGTTGGGACTGATTCATTC CTCCACGATATGCCTCCTCTCTCTGATATCCTGCTAACTGTAGCCGT TGTGGCATTTGAGATGACAGGACATATATATATATGGCCCCACACT TGACCTTGAGTGCCTGAATGCTCTGAAATCAAGCATATGGCACAGC GCTCAAGACTTTTG 628 NON_CODING CCAGACTCGAGAGGTGGGAGGAACTCCTTGCACACACCCTGAGCTT (UTR) TTGCCACTTCTATCATTTTTGAGCAACTCCCTCTCAGCTAAAAGGCC ACCCCTTTATCGCATTGCTGTCCTTGG 629 NON_CODING TGAAATAATTCATGCCACGGACCTGTGCACATGCCTGGAATTGAGA (UTR) GACACAGTTAAAAGACTCCAAGTTGCTTTCTGCCTTTTGAAAACTC CTGAAAACCATCCCTTTGGACTCTGGAATTCTACACAGCTCAACCA AGACTTTGCTTGAATGTTTACATTTTCTGCTCGCTGTCCTACATATC ACAATA 630 NON_CODING CTGTGCTTTTACCAGTAGCATGACCCCTTCTGAAGCCATCCGTAGA (UTR) AAGTACTTTGTCCTCCAAAAAGCTAACATACGGTTTTGAAGCAGCA TTGAAACTTTTGTAGCAATCTGGTCTATAGACTTTTAACTCAAGAA GCTAAGGCTAGACTTGTTACCTTCGTTGAA 631 NON_CODING AGAGGAGGGGACAAGCCAGTTCTCCTTTGCAGCAAAAAATTACAT (UTR) GTATATATTATTAAGATAATATATACATTGGATTTTATTTTTTTAAA AAGTTTATTTTGCTCCATTTTTGAAAAAGAGAGAGCTTGGGTGGCG AGCGGTTTTTTTTTTAAATCAATTATCCTTATTTTCTGTTATTTGTCC CCGTCCCTCCCCACCCCCCTGCTGAAGCGAGAATAAGGGCAGGGAC CGCGGCTCCTACCTCTTGGTGATCCCCTTCCCCATTCCGCCCCCGCC TCAACGCCCAGCACAGTGCCCTGCACACAGTAGTCGCTCAATAAAT GTTCGTG 632 NON_CODING AGCCATCGGTCTAGCATATCAGTCACTGGGCCCAACATATCCATTT (UTR) TTAAACCCTTTCCCCCAAATACACTGCGTCCTGGTTCCTGTTTAGCT GTTCTGAAATACGGTGTGTAAGTAAGTCAGAACCCAGCTACCAGTG ATTATTGCGAGGGCAATGGGACCTCATAAATAAGGTTTTCTGTGAT GTGACGCCAGTTTACATAAGAGAATATCACTCCGATGGTCGGTTTC TGACTGTCACGCTAAGGGCAACTGTAAACTGGAATAATAATGCACT CGCAACCAGGTAAACTTAGATACACTAGTTTGTTTAAAATTATAGA TTTACTGTACATGACTTGTAATATACTATAATTTGTATTTGTAAAGA GATGGTCTATATTTTGTAATTACTGTATTGTATTTGAACTGCAGCAA TATCCATGGGTCCTAATAATTGTAGTTCCCCACTAAAATCTAGAAA TTATTAGTATTTTTACTCGGGCTATCCAGAAGTAGAAGAAATAGAG CCAATTCTCATTTATTCAGCGAAAATCCTCTGGGGTTAAAATTTTAA GTTTGAAAGAACTTGACACTACAGAAATTTTTCTAAAATATTTTGA GTCACTATAAACCTATCATCTTTCCACAAGATATACCAGATGACTA TTTGCAGTCTTTTCTTTGGGCAAGAGTTCCATGATTTTGATACTGTA CCTTTGGATCCACCATGGGTTGCAACTGTCTTTGGTTTTGTTTGTTT GACTTGAACCACCCTCTGGTAAGTAAGTAAGTGAATTACAGAGCAG GTCCAGCTGGCTGCTCTGCCCCTTGGGTATCCATAGTTACGGTTTTC TCTGTGGCCCACCCAGGGTGTTTTTTGCATCGCTGGTGCAGAAATG CATAGGTGGATGAGATATAGCTGCTCTTGTCCTCTGGGGACTGGTG GTGCTGCTTAAGAAATAAGGGGTGCTGGGGACAGAGGAGCAACGT GGTGATCTATAGGATTGGAGTGTCGGGGTCTGTACAAATCGTATTG TTGCCTTTTACAAAACTGCTGTACTGTATGTTCTCTTTGAGGGCTTT TATATGCAATTGAATGAGGGCTGAAGTTTTCATTAGAATGCACTCA CACTCTGACTGTACGTCCTGATGAAAACCCACTTTTGGATAATTAG AACCGTCAAGGCTTCATTTTCTGTCAACAGAATTAGGCCGACTGTC AGGTTACCTTGGCAGGGATTCCCTGCAATCAAAAAGATAGATGATA GGTAGCAATTTTGGTCCAAAATTTTTAATAGTATACAGACAACCTG TTAATTTTTTTTTTTTTTTTTTTTTTTGTAAATAACAAACACCACTTT GTTATGAAGACCTTACAAACCTCTTCTTAAGACATTCTTACTCTGAT CCAGGCAAAAACACTTCAAGGTTTGTAAATGACTCTTTCCTGACAT AAATCCTTTTTTATTAAAATGCAAAATGTTCTTCAGAATAAAACTGT GTAATAATTTTTATACTTGGGAGTGCTCCTTGCACAGAGCTGTCATT TGCCAGTGAGAGCCTCCGACAGGGCAGGTACTGTGCCAGGGCAGC TCTGAAATTATGGATATTCTTATCCTCCTGGTTCCTTCGGTGCCAAT GGTAACCTAATACCAGCCGCAGGGAGCGCCATTTCTCCTAAAGGGC TACACCACTGTCAACATTATCCTGGACTCTGTGTCTCTCTCTGTTGG GTCTTGTGGCATCACATCAGGCCAAAATTGCCAGACCAGGACCCTA AGTGTCTGATAGAGGCGATGATCTTTTCCAAAGTCAGTACTTACAA ACTGGCATTCTTACAGGCTGCACCATTTCCTAGTATGTCTGCTTTAA GCCTGGTTCAACCTCTCATCGAATA 633 NON_CODING CCAGTCGCTGTGGTTGTTTTAGCTCCTTGACTCCTTGTGGTTTATGT (UTR) CATCATACATGACTCAGCATACCTGCTGGTGCAGAGCTGAAGATTT TGGAGGGTCCTCCACAATAAGGTCAATGCCAGAGACGGAAGCCTTT TTCCCCAAAGTCTTAAAATAACTTATATCATCAGCATACCTTTATTG TGATCTATCAATAGTCAAGAAAAATTATTGTATAAGATTAGAATGA AAATTGTATGTTAAGTTACTTCACTTTAATTCTCATGTGATCCTTTT ATGTTATTTATATATTGGTAACATCCTTTCTATTGAAAAATCACCAC ACCAAACCTCTCTTATTAGAACAGGCAAGTGAAGAAAAGTGAATG CTCAAGTTTTTCAGAAAGCATTACATTTCCAAATGAATGACCTTGTT GCATGATGTATTTTTGTACCCTTCCTACAGATAGTCAAACCATAAA CTTCATGGTCATGGGTCATGTTGGTGAAAATTATTCTGTAGGATAT AAGCTACCCACGTACTTGGTGCTTTACCCCAACCCTTCCAACAGTG CTGTGAGGTTGGTATTATTTCATTTTTTAGATGAGAAAATGGGAGC TCAGAGAGGTTATATATTTAAGTTGGTGCAAAAGTAATTGCAAGTT TTGCCACCGAAAGGAATGGCAAAACCACAATTATTTTTGAACCAAC CTAATAATTTACCGTAAGTCCTACATTTAGTATCAAGCTAGAGACT GAATTTGAACTCAACTCTGTCCAACTCCAAAATTCATGTGCTTTTTC CTTCTAGGCCTTTCATACCAAACTAATAGTAGTTTATATTCTCTTCC AACAAATGCATATTGGATTAAATTGACTAGAATGGAATCTGGAATA TAGTTCTTCTGGATGGCTCCAAAACACATGTTTT 634 NON_CODING TGTTGTTGCAATGTTAGTGATGTTTTAA (UTR) 635 NONCODING AAATAATGCTTGTTACAATTCGACCTAATATGTGCATTGTAAAATA (UTR) 636 NON_CODING GTTTGCCCTTTGGTACAGAAGGTGAGTTAAAGCTGGTGGAAAAGGC (UTR) TTATTGCATTGCATTCAGAGTAACCTGTGTGCATACTCTAGA 637 NON_CODING CAAAGTAAACTCGGTGGCCTCTTCT (UTR) 638 NON_CODING CGAGGTGATGGGACTTCTTAACACACATTTCTATAATACCCATGAA (UTR) ATGATAATTTGTAAAATAACACTTAGTGATATCTGGAAATAATAAT TCAATTAAGCAACCACGAATTTCACCCTGGAGATATTTTTTCTTATT TGAGTCCACCAAAGGATAATGCCAACTTATATAAGTTCTCAAATCA TGCCTTCCGCTTAGTCTCATTTTATTCATTCAGTCGTCATGAGTTGA GTGCTTACTACATGCAAGGCACTCTGCTAGTTATATTCTAATAATGC AGAGATAATTAGACATGGTTCCCGCCCTCA 639 NON_CODING TTCCATACACGTTTGCAGTTTCTTGTACACATTTGGATACTTTGAAA (UTR) GATGACAGATTGTTAAATCCATTCAATGGTAAAGAAACTCACCATC TGGAGATTGAGTCTACTTGTTAATGAATGACTAGCCCAATTATCCTT ATAAATTGAATATGGTGACCAAATGCTTTGATATCATACTACTCTG CCTTTGTGGGCACATATGTAGACACTACTAAAAATAAATATTTTTG GAGATTAAAATGGAGAATAGAAGTAATTACATTATTTAGGTCTTAA TCCAACTTTTTTCTAATATATCTAAACAATTGAAAGGGAAGCTTATT CATGGAATATTGGCTTGATTTATCTAGAAAGTTTTTCCTTCTTCAAT TTTACTATATTCATTCTACAGGAACAGCAATAAGTACTATTAAACA GAAGATGGCTACACTAAGTTCCAATTTTGTTGCTGAATTGCTTCTGT GAGTTCACTTTTCAGTTCTAAGGAAGAATAATATTTGCTACATATTT CACAGGGGTTCTTA 640 NON_CODING CCCACCTTTCCATGCTTAAGACAAAAATGTCTTAAATATAAAGCTG (UTR) TGATTATATCAAAAATCCAGATAAATCATCAAATATATCAGATTAA GACCAGGGTTTACACACTTAGGCAATAGTC 641 NON_CODING GTTTTAATTCAACAGTCCAACATTATTTAGGTGTTACAGAGTGTAA (UTR) ATATATTTCTTTGGGAGTTATTTTCTTTTTAAAATCTTTTTATAGCTT GGCAATGTCCAAAGTCAAATATCACCTAAACTGGTTAGATTACTTC TACAGCTAATAATATTGCAG 642 NON_CODING TGGCTACTTGACCTACAGCAAAAGCCATTTCTGTACCATAAAAATT (UTR) TGTTGTGCAATATTAGAATTATCATATGTTTCCTACATCTGACAGCA CCTAAAATGTTTGATAATATTAACATGTATCTAAGAGGAAAAAAGA GTTAATATATTCTGGCACCCACTTTCCTAGTAATGTTTTCCATGATT TTCCAGTTCTGAGGCACTTATTAAAGTGCTTTTTTTTTTCTGAATTA ATTAGGTATTGGTAAAATATATTTTTAAATTTAGTTAGCTTTATAAA CACAATTAGAATTACAATTAATTAACAGAGGTATAATTGTCTCACT TTCAGAAGTGATCATTTATTTTTATTTAGCACAGGTCATAAGAAAA ATATATAGAAAAATAATCAATTTCATATATAAAAGGATTATTTCTC CACCTTTAATTATTGGCCTATCATTTGTTAGTGTTATTTGGTCATATT ATTGAACTAATGTATTATTCCATTCAAAGTCTTTCTAGATTTAAAAA TGTATGCAAAAGCTTAGGATTATATCATGTGTAACTATTATAGATA ACATCCTAAACCTTCAGTTTAGATATATAATTGACTGGGTGTAATCT CTTTTGTAATCTGTTTTGACAGATTTCTTAAATTATGTTAGCATAAT CAAGGAAGATTTACCTTGAAGCACTTTCCAAATTGATACTTTCAAA CTTATTTTAAAGCAGTAGAACCTTTTCTATGAACTAAATCACATGC AAAACTCCAACCTGTAGTATACATAAAATGGACTTACTTATTCCTC TCACCTTCTCCAGTGCCTAGGAATATTCTTCTCTGAGCCCTAGGATT GATTCTATCACACAGAGCAACATTAATCTAAATGGTTTAGCTCCCT CTTTTTCTCTAAAAACAATCAGCTAATAAAAAAAAAATTTGAGGG CCTAAATTATTTCAATGGTTGTTTGAAATATTCAGTTCAGTTTGTAC CTGTTAGCAGTCTTTCAGTTTGGGGGAGAATTAAATACTGTGCTAA GCTGGTGCTTGGATACATATTACAGCATCTTGTGTTTTATTTGACAA ACAGAATTTTGGTGCCATAATATTTTGAGAATTAGAGAAGATTGTG ATGCATATATATAAACACTATTTTTAAAAAATATCTAAATATGTCTC ACATATTTATATAATCCTCAAATATACTGTACCATTTTAGATATTTT TTAAACAGATTAATTTGGAGAAGTTTTATTCATTACCTAATTCTGTG GCAAAAATGGTGCCTCTGATGTTGTGATATAGTATTGTCAGTGTGT ACATATATAAAACCTGTGTAAACCTCTGTCCTTATGA 643 NON_CODING TTCATCAACTCAGTCATCAAATTCC (ncTRANSCRIPT) 644 NON_CODING TCTTCCCATGCACTATTCTGGAGGTTT (UTR) 645 NON_CODING GCACACTCTGATCAACTCTTCTCTGCCGACAGTCATTTTGCTGAATT (UTR) TCAGCCAAAAATATTATGCATTTTGATGCTTTATTCAAGGCTATACC TCAAACTTTTTCTTCTCAGAATCCAGGATTTCACAGGATACTTGTAT ATATGGAAAACAAGCAAGTTTATATTTTTGGACAGGGAAATGTGTG TAAGAAAGTATATTAACAAATCAATGCCTCCGTCAAGCAAACAATC ATATGTATACTTTTTTTCTACGTTATCTCATCTCCTTGTTTTCAGTGT GCTTCAATAATGCAGGTTA 646 NON_CODING TTTCCAAAACTTGCACGTGTCCCTGAATTCCATCTGACTCTAATTTT (UTR) ATGAGAATTGCAGAACTCTGATGGCAATAAATA 647 NON_CODING GCTTCAGGTGACCACAATAGCAACACCTCCCTATTCTGTTATTTCTT (UTR) AGTGTAGGTAGACAATTCTTTCAGGAGCAGAGCAGCGTCCTATAAT CCTAGACCTTTTCATGACGTGTAAAAAATGATGTTTCATCCTCTGAT TGCCCCAATAAAAATCTTTGTTGTCCATCCCTATA 648 NON_CODING (UTR) GTTTCGACAGCTGATTACACAGTTGCTGTCATAA 649 NON_CODING CTGGCAATATAGCAACTATGAAGAGAAAAGCTACTAATAAAATTA (UTR) ACCCAACGCATAGAAGACTTT 650 NON_CODING TCTCTAGCTATAAGTCTTAATTATACAACAAAATACTATTTTTATAT (UTR) TTATGTTTGGTAAATTCAATAACTTTCCTCATCATTTGGAAAGTCAA ATTGTTTATTGCTTCCCTACAGTTTTTTCTGAATC 651 NON_CODING CTGGGATTCTTACCCTACAAACCAG (UTR) 652 NON_CODING TTCAAAGAAATACATCCTTGGTTTACACTCAAAAGTCAAATTAAAT (UTR) TCTTTCCCAATGCCCCAACTAATTTTGAGATTCAGTC 653 NON_CODING AGGGAAAAGTTAAGACGAATCACTG (INTRONIC) 654 NON_CODING ATCTTCCAACAACGTTTGTCCTCAAAT (INTRONIC) 655 NON_CODING CCTATTACAGCTAATCTCGTTTTAAATCTGCTC (UTR) 656 NON_CODING TATGTAACAATCTTGCACAGTGCTGCTAATGTAAATTTCAGTTTTTC (INTRONIC) GCCTCTAGGACAAACA 657 NON_CODING TTTGAAGTCAACTGTATCACGTCGCATAACCTAATCACAAAAGTAA (INTRONIC) TATCCACAAAATTAATAGTCCTACAGATGATGTAGGGTGTGTACAG CAGGAAGCAGGAAATCTTGGGGGTTGTCATAGAATTCTGCTAAATA TGCCTAGAGACACACATCCTTAACTGGACTTTAGGTTTATCATTTGT GTTCTCTGGCCTCAGTGTTTTCAATTTGTGGATCATGTACCAATAGC ATC 658 NON_CODING GGCCTCATTAATATAGTGGCTGATGGTACCTACTAACCTTCAATGG (INTRONIC) GTCGCCTCCTACCTATTCTCATTTCATTAGCTTTTTGAAGGACAGGG TAGACTAGATCAAGAAAAGAGATAAAAAGAAATAGTACATATTCA CACTTATGTAATTACATCCCCTTCCATGGAAACTTGGGAATAAAGA GGTATTTCAAGGTCATGTAGAAAAAGTAAAC 659 NON_CODING GTTGTGGGGATTAAGACATTAATTC (INTRONIC) 660 NON_CODING TCTCACTTTGCATTTAGTCAAAAGAAAAAATGCTTTATAGCAAAAT (UTR) GAAAGAGAACATGAAATGCTTCTTTCTCAGTTTATTGGTTGAATGT GTATCTATTTGAGTCTGGAAATAACTAATGTGTTTGATAATTAGTTT AGTTTGTGGCTTCATGGAAACTCCCTGTAAACTAAAAGCTTCAGGG TTATGTCTATGTTCA 661 NON_CODING AGCCCTCACTCTAAAGTCACTTGTCACACATTCTATCAAATAAGGG (INTRONIC) AGAAAAAAACAAACACTATATCCAATTATAGTTTTCCACCTGAAAC TACCAAAATAGAAAAAAAAAATTTTCCTATTAAAATGGAAAAAGT CTAAGTGCTCAGGTAGAATCATTGAATTATCATTTTTGCTAGAGTTG ACCTTATGCATTTCAAGGCTGGCACCATCATGTACAGGAACAATAT GCTCATTGCTCCTCCCACCCATCCCCACCATGATGAAGAAAAGAGC TGATTAGTGAACAACTAATAAATATGTGCCATCTGGGTACTAGTAA CTTTA 662 NON_CODING CAGGTATAAGGTTAGATGCTACATCTAGGAGCATTCAAGATATACA (UTR) TTAATTTAAACTTTTATTAGTCTAACTTTCTGTTAAGTCTCTTAGCTT TGAAACATAAAAGAGAAATCAAGCCCAAATTTTTAGAGGAAGGCT AAGGTATACTATTGGCAGTTGTAGTTTTAATTGTAATTGACTGATTA ACCAAGTAATTTATAAAATGTTACCTATACTGTCAGTG 663 NON_CODING CCGACTAACATGGTAATAGACCTGAATGCATAATGAGTTCTTACTT (UTR) TGCTATCATCAAAAGACTTTTCATCACAGTTACATACTTTCTAATTT ATGGAAAAACAGCATTTGGAAAACAAATGTTTTGTTTTTATTTTTTT AAAGATTTAAAAAATAAATCAACTAGGGACTAGGAATCAACAACT GTGAGTGAGTTAAACTGTGTTGAAATACTAAAGGGTTGT 664 NON_CODING TTCTTGCCTAAACATTGGACTGTACTTTGCATTTTTTTCTTTAAAAA (INTERGENIC) TTTCTATTCTAACACAACTTGGTTGATTTTTCCTGGTCTACTTTATGG TTATTAGACATACTCATGGGTATTATTAGATTTCATAATGGTCAATG ATAATAGGAATTACATGGAGCCCAACAGAGAATATTTGCTCAATAC ATTTTTGTTAATATATTTAGGAACTTAATGGAGTCTCTCAGTG 665 NON_CODING CTAGAGTTCTCATTTATTCAGGATACCTATTCTTACTGTATTAAAAT (UTR) TTGGATATGTGTTTCATTCTGTCTCAAAAATCACATTTTATTCTGAG AAGGTTGGTTAAAAGATGGCAGAA 666 NON_CODING GTGCTAGTTGATATCATGATTGATTTGGTCTTCTTGG (INTRONIC) 667 NON_CODING TTACGTTAGTACTGCAGAGGAAATAACTTGGAAGTTACAGGGAATA (INTRONIC) ACAATAGGTACTAGAAATTGAGTGCTATGGGTACGTATTAGATCGT TAGCTCATTTAGTATC 668 NON_CODING CTATAGAAGGTTATTGTAGTTATCTTTAGTACTATGTTATTTTAGGA (INTRONIC) GGCCTGTGTTTAAATTTTACAATTCATTAACAGGACTGATGGCATTT TGTAGGAACTACTTAGGAACAAGTTTGCATTTC 669 NON_CODING GACACTTAGGTGATAACAATTCTGGTAT (ncTRANSCRIPT) 670 NON_CODING GGGCTCTCTAGAAAGGTAATTATTATCTGATATAATAGTTTAGTCT (INTRONIC) GTGATGCTTCTTTTAACATATTTGTAAGTTTTAACCAAATGGTTAAA GAAATTTGCTTTTTAACCCTTAAACCTCACATATCCACAAGTCTCTA AATTCCATAGGATGCTATGGATTTCTAGTTGCCTAGTTCATGTCTTT TACTTAGAAAACGTCAGAAAACCCAAACTTCTCGTGACTTCAAAAA GTGTAATTGTACCTGAAACTTCTTTTCCTTCAGATTTCTTATTTATGT TTTCTGATAGGTTTTTAAGATTAATCTTTTCAGAAGGATGCTCTAAA AATCTGGCCAATTTGATTATCCTCTTCCAACTTGGAAAAAATATGT ATTTAAAATGAGACTAGAATTTGAATGACCTTCTTTCATGGAACTC TGA 671 NON_CODING GTTGTTGCCTCTAACATGTATAAAGG (UTR) 672 NON_CODING AAGTCATTATCTTGCTTTGGAATCATTATCTGGCATTATCAACTTGC (CDS_ANTISENSE) ATTTGGTTCCACAACA 673 NON_CODING GTGAGAAAAAACAAGTCATATAAAA (INTRONIC) 674 NON_CODING AGGAATAATTGATCAAGATGACATAAAATTTACAAATTTATTTGTG (INTRONIC) CCTAATAATAGTCTCAAATTACATAAGGCAAAAACTGATAGAATGA AAGGAAGAAATAGGCAATTATAATTGGAAATTTTAATGTCTCTCAG AAGTTGATAGAGTAACCAACAAAAAATCAGCAGACAGAAAACCTG AACAACATTATCAGTCACTTTGA 675 NON_CODING CTGGGCCCTTTACAGTTGATACCCAAAGCAG (INTRONIC) 676 NON_CODING TCTGGGTACTAGGAGTAGACCATCCATTCTTGATTTGAACTGTTTCT (INTRONIC) GCAGGTACTCATTTGTTCAAACACTGCCTATTTCGTTTTGCAACAGA TCTATTTTAGAAAATCTTTATATTGAGCAAACAGCAGTCTCACTATA GCCTCTACTTGTTGGTCATAATCTGCCAGAGGAAGCTTACCTGATG ATGATGGTGCTGCTGCTGCTGATAATGATGGTGATGGTAATGACGA ACATGACACAAGATCACAGGCACTGTGCTAAGCATTAAACACATA CAATCTTATTTAATCCTCATAATGTTATGGCATAAATATTACCCCTC TTTTAAAGATGAACAAACAGATGATTAAAGGGGTAAAGTTGCTTTG ATCTTTAATATTAATTTGTGTCTTTCTCACTTCAAATTCAGCGATGA ACCCTATTCCTATG 677 NON_CODING CCTTTGATCTTAAGATTGTTGGCAT (INTRONIC) 678 NON_CODING ACTGTGGCTTCAATAGCCTCATAGAAGTGTCCTTCCTTTTTAACAAA (INTERGENIC) GGGAATCCAAGATGGCGGAAAGGTCCTAACATTGAGCATATAATC CATCTCTTTGCTAAACTAGATGTTTCCTTCCAGATTTCTATG 679 NON_CODING ATGGAAGCAAAAGGGACAGACTTGAAGCTGTACTTCCAGACTCTC (INTRONIC) ATGGAAGCTCCAG 680 NON_CODING GAGCAATGCTTAACCCATCGGAATGTATACCCTAAGCAAAACTGTC (INTRONIC) AACCAGGCAAAGGGTGTTCTTTCTCTTCTGGCGCTCTGCTCTTCGTC CCTGTCCCCAGCAGCCCATCTGCTACTGGAACTTGTTCACAGAGTC CTTCTGCCAACTTATCATATTCTTGTTCCAGGAACTTTTCTGCTTTA AGTAAAGGATCTTCTCCCAACGAGTATGCTCCTGCATTTGCAGATA CAGCACAGCTCCATGCATTTGTAGCCCTGCCATATTAGTGTCCTAG C 681 NON_CODING CCCTAGGTAGGAGATAACAAGTATGTACCATTACTGAATATTAAAT (INTRONIC_ CCTTCTTTACCATAGCTACAGTTAAGTAGGTGTATCTCAGAAACCT ANTISENSE) AAGGTAGTTTTAAATGTAGTGAAATTGTCCACAGCAAGCTGGCCCA AGTGCTCACATTTTATACCCGCTCTGTCTTAGTGCGTTGCAAGAGA GGAGTATATACAGTAGTTCCCCCTTATCCACAGGGGTACATTCTAA GACCCCCGGTGGGTGACTGAAACCACAGATAGTACCGAATCTTATA CATACTATGTTTTTTTTCTAAACATAAATACCTACAATAAAGTTTAA TTTTTAAATTAGGCACCATAATTAATAATAAAACAGAACAGTTATA ACAATATACTATAATAAAATTATGTGTATGTGATCTCTCTTTCTCTC TCCCTCTCAAAATATTTTTAATATCTCTCCAGAATTCAGTGCAAATA ATTCCATCATACTCACTTCAGAAAAGTGAAGATAGTCTTGTACATG AGTAGATTCAAATTTTATTGTCGTGGTTTCCAAAGTTTTATTTTTCT CACCAATGGAACTTTTGATTCAAATAAAATATCCAAGGGATTTCAG CTTATAAAACACACAAAATTGATAATGAGTTTTCCAAGGTACTGTG TGTGTGAATGTGTATGTCTGTGTATGTGTGTGTCGTCTGTATGTTTT TCCCACCTCTTGTAGAAGCTACGAAGCACCTTTCCATATTATTGAG GTTTCCTGTACGTAGACTGA 682 NON_CODING ACCTGGACTGAAGTTCGCATTGAACTCTACAACATTCTGTGGGATA (UTR) TATTGTTCAAAAAGATATTGTTGTTTTCCATGATTTAGCAAGCAACT AATTTTCTCCCAAGCTGATTTTATTCAATATGGTTACGTTGGTTAAA TA 683 NON_CODING CAGTATATGATATGGCAGAGTTGCACAGAAGAATCAGAACATTGTT (ncTRANSCRIPT) TTAGAGAAACGTTGGGCAATTAATTAAGCCAGCTGATTAAGTTTTA A 684 NON_CODING TTCACCACTGTAGATCCCATGCATGGATCTATGTAGTATGCTCTGAC (UTR) TCTAATAGGACTGTATATACTGTTTTAAGAATGGGCTGAAATCAGA ATGCCTGTTTGTGGTTTCATATGCAATAATATATTTTTTTAAAAATG TGGACTTCATAGGAAGGCGTGAGTACAATTAGTATAATGCATAACT CATTGTTGTCCTAGATA 685 NON_CODING GCCAAAACCAATATGCTTATAAGAAATAATGAAAAGTTCATCCATT (UTR) TCTGATAAAGTTCTCTATGGCAAAGTCTTTCAAATACGAGATAACT GCAAAATA 686 NON_CODING TTCCAAATACTCATGGTGCACAAGAAGGTTATGTATGCACAGTATT (UTR_ANTISENSE) TCTAATTTATTCAAATTCAATTTGAATTTGGTCTGAAGCTATCTTGT ATGAAATGTTAGCTTTCCTGATATTTAATAATATTTATTATGTTTGC ATATAAGCTCAAAAAATTAATGCAAAAGTATACTTTACTCATGGTT ATCTTCAGGTAAATATTAGTGGTTATGTTTAAAAGCCTGATTTTATA TAGATGAAGTTGAGAAAAAAAAAGAGTATGGAAAGGTAAATTAGG TCTTAGTCTTGATTCTGTTACCAGCTGTTTGACCTTGAGTAACTCTT CACCCTTCAATGGGCCCCAGTTTGCTCCTCTATGAATTTTAAGGGGT TGGACTAGTTGACAGACCAGGCCCCTTCCAAGTCTAACATTTCAAA ATCCTAACATTCCAGGTTCTATCATCTTGATA 687 NON_CODING TTGTATTTTGCATACTCAAGGTGAGAA (UTR) 688 NON_CODING GATCTACCATACCCATTGACTAACT (UTR) 689 NON_CODING GAGATACATCATCATATCACGGAAAG (ncTRANSCRIPT) 690 NON_CODING ATCAGCTTTGAGTGAACTTTGACAGAAG (UTR) 691 NON_CODING CCTGTACCCTTATGCAGAGCAAGCATTCCATCCTAAGTTATAAACT (UTR) ACAGTGATGTTTAATTTTGAAGCCAGGTCTACATTATTTAATTAATG GCTTCAAAAGGTGGAGATGCACTTTATTTAATGTCTTTCCCTAGCTA ATTCTTACTCTCACCTTAAATATGCTTTCTTGTTGCATATATGCACA GATACACACACACACACACACGAAAATAAATAAATGTTCATATTCT TCTGTTCAACAGACATTTATTTTCTCCTCTCCCTTGAATAAGAAAAT AAGTTTTCCATTCCTATGAACTGTCTAATATCTTTCTATTACAGAAG GGGAAACTGAGGCTGGGAAAGGCTAAATGACTTATC 692 NON_CODING GTCCTCAGTGTACCACTACTTAGAGATATGTATCATAAAAATAAAA (ncTRANSCRIPT) TCTGTAAACCATAGGTAATGATTATATAAAATACATAATATTTTTC AATTTTGAAAACTCTAATTGTCCATTCTTGCTTGACTCTACTATTAA GTTTGAAAATAGTTACCTTCAAAGGCCAAGAGAATTCTATTTGAAG CATGCTCTGTAAGTTGCTTCCTAACATCCTTGGACTGAGAAATT 693 NON_CODING CTGGTTAATTAGCAATTTAAGACCAGAGCCAAATTATCCCAAGAGC (ncTRANSCRIPT) ATACATTCTTTTGGTTTTCCTAACTTTGTGAAAAAAATTGATGCAGC TGTTTTTAACCCACGTTTTTATAGGACCTACTTCTTTGTAGATAACC A 694 NON_CODING TGATGCTGTCACTACCGTGGGAAATAAGATCTTT (ncTRANSCRIPT) 695 NON_CODING CACCTGACATGAACCGTGAGGATGTTGACTACGCAATCCGGAAAG (ncTRANSCRIPT) CTTTCCAAGTATGGAGTAATGTTACCCCCTTGAAATTCAGCAAGAT TAACACAGGCATGG 696 NON_CODING AGATAAACAAACTTCCAGTGACAAA (ncTRANSCRIPT) 697 NON_CODING TGCTTCAAGCCAATGCAAAAAGTTCATACATTATATTCCCTATTTCA (UTR_ANTISENSE) TTGTGTTTAGAATATATTATATTGTTTAAATGCCACTACCACAGTGT AATTTTTTTTTTTTTAATACTGAATCTCTGGAATAATGGTAAGGTCA AAATATATTGTATTGAGAGTTTAAAAATTAAGAGCAATTTTTAAAA ATGTAACAAACATCTAAATATCTGACAATAAAATCTGAAATGCTGT AACTTCAACATTAACTGCACCATCCAAATTCTTGTGACTTACGCATT TTTGCCCAATTTAACCTTTCTGATGTTCCCCTGCCCCCAGACACCAT AAATGCATTGTAA 698 NON_CODING TTCCAGGACTGTCATAATGATCTGTACTTCC (INTERGENIC) 699 NON_CODING CTGCTGTGGTTTGTAAGAACTCATTGACTAACTCAAGGTCACAAAA (INTERGENIC) ATTTTCTCCTTTATTTTTTTCTAGACATTTTATAGCTTCAGGTTTTAT ACTGAGGTCTATGATTTATTTGGGATTAATTCGACAAATGTAAATTT GTCGAAAAGACTATTTTTCTTTACTAAATTGCTTTTGCACCTTTATC ACCAATCAGTTGTCTGTATATTCATGGGATTATTTCTAAACTC 700 NON_CODING ATTTACAGCTTGTAGCAATTATGTA (UTR) 701 NON_CODING CTACCATAAAGTCCGTAAGTGAATACAACGAATGTAATTGACATAA (UTR) TAATTGAAAATCATTGACTATACCTAAAATAGTTC 702 NON_CODING GCTCTGGCTATATCAAATAAAAGTGTCAAGAGTGAGCATCCTTGCC (ncTRANSCRIPT) TTGTGCTGAATCACAAAGGAATACCTTTCAGTTTTTCTCCATTGATT ATGATAGCAGTGGGCTTTTCACAGTGGGCTTTACT 703 NON_CODING TCTTAGCATCCAATCTTATGGACCATTTTCATACAAAGCC (INTRONIC) 704 NON_CODING CTCCAACAATAAAGCACAGAGTGGAT (UTR) 705 NON_CODING TTAGATGTCATTGAATCCTTTTCAA (UTR) 706 NON_CODING TTCTTAAAGTTTGGCAATAAATCCA (UTR) 707 NON_CODING GTGGCCACATCATGCAAATATAGTCTCACCATTCCTAGG (UTR) 708 NON_CODING TCTTGGCAGAACTGCTCTATTGCTCAAGGAAGACTTAGTTTCTGGA (INTERGENIC) AATATTCCCCGGGTGAGTTAAGGGTTGTGTAAAAATGCAAGAATGG AATACGAAATGATTTTCATTTTGATGGTTACTTATGAAGTTTTTGTG TTCCGTAGAA 709 NON_CODING CATTCATCTTTGAATAACGTCTCCTTGTTT (UTR) 710 NON_CODING CAGAGCCAGATCTTTAGACGTGATGGATTCCCAAGTTTCGTTCTTA (INTRONIC) AAATAGACAAACTGAGGCCAAGAGTGCACCAGCCTGCCAAGCACA GACATGACACCTAAGGACTTTCCTCCCCTAAGTGTGTGGTTCTGGG GAGCCAGCCTTCCTTTGTCCTTCATAACCCCAGTCACTGCCTTTCCA GCCTTCTGCCAGGTCTGGGGCTCAGATGGAGATAAGCTTTTCACAG AAGACCCTCACTCGAAAGATCCACCACTTATCTCCCATCTCCGACA GTGCATG 711 NON_CODING ATGTATTTTGTAGCAACTTCGATGGAGC (CDS_ANTISENSE) 712 NON_CODING CTGACACGACACTTTTCTGTGGTTTC (CDS_ANTISENSE) 713 NON_CODING GTACAATCACTACAACATGCTCTGCCACCCACTCCTTTTCCAGTGAC (UTR) ACTACTTGAGCCACACACTTTC 714 NON_CODING CGTCTTTGGTCAGGAACTTTATAATGTGCTAT (UTR) 715 NON_CODING AGCAGCCTTGACAAAACGTTCCTGGAACTCA (UTR) 716 NON_CODING GCTATCCACAGCTTACAGCAATTTGATAAAATATACTTTTGTGAAC (UTR) AAAAATTGAGACATTTACATTTTCTCCCTATGTGGTCGCTCCAGACT TGGGAAACTATTCATGAATATTTATATTGTATGGTAATATAGTTATT GCACAAGTTC 717 NON_CODING TTTGACTAGAATGTCGTATTTGAGGATATAAACCCATAGGTAATAA (UTR) ACCCACAGGTACT 718 NON_CODING TGCAAAATAACGACTTATCTGCTTTTC (INTRONIC) 719 NON_CODING GCAATAGAAGACACGTCTAGCTTGAA (INTRONIC) 720 NON_CODING GAACCATTGGAGATACTCATTACTCTTTGAAGGCTTACAGTGGAAT (INTRONIC) GAATTCAAATACGACTTATTTGAGGAATTGAAGTTGACTTTATGGA GCTGATAAGAATC 721 NON_CODING AGCGACCACATAGGGAGAAAATGTAAATGTCTCAATTTTTGTTCAC (UTR_ANTISENSE) AAAAGTATATTTTATCAAATTGCTGTAAGCTGTGGATAGCTTAAAA GAAAAAAAGTTTCCTGAAATCTGGGAAACAAGACATTTAAAGAAT CAGCAAAATTTCAAATAAAAAATTATGAAAATATTATCCTCATTAG TTCATTTAGTCCCATGAAATTAATTATTTTCTCTGCTTGATCTTGGT GGACAGTTTCATGAAGCTGTCAGTTAGTTCATTAAAGTTTTGGAAA TTCTCAGACAGTGCAGTGGTATCAGAAACTTGTATTCAAGAGTACA GGTCAGA 722 NON_CODING ATGCCTCATATTGTATCTAGATTGGTCTTAAACATGCTCTGCACTTC (INTRONIC) TCTGCCTTCATGGAAGACTTTTGCTGATATTTCCTTCACTTGATACA CTTTTGGCTTTTCCACCCTCTCCCTGCCCCCAATTTCTGCTTGCCAG AATAATATCTGTTCTTCTTTCATTCATTTATTTAACAACTATTGAGA CACTGTTGTAGGTGCTTGGATACACCTAGTGAACA 723 NON_CODING AAAGAAGTGAAGCAAACGGATGGGA (INTRONIC) 724 NON_CODING TTCTGATGCTGTATTTAACCACTATA (ncTRANSCRIPT) 725 NON_CODING CTGCCTCAGGGTAATCTGAATTTTCTATCTCAAGTTAGAGATTACTC (INTERGENIC) TTCACCCCTTCCCAAGCAGATATTAAAGTCTCTTATTCTGTTTTTTTC CTTTAAAAAGTATCAGATCTGTCAAGAGTTGTTTCTTCAGAATCTTC TATTGCCAAAAACTGTTCTTATAATCTATTTTATCATTCACTCACTT TGTCACTGATTAACATATTAGCACCAAAGTTCAACCAATGCTTAC 726 NON_CODING TTTGCAAAAGCACGGATGTGGATGA (INTERGENIC) 727 NON_CODING ATGTCCATGTCCATCTTAATGTCTTT (INTRONIC) 728 NON_CODING AGGTACTGAATGACTAGGAAACAGGAA (ncTRANSCRIPT) 729 NON_CODING GAGCACCTGATCTTCGGAGATGCCTG (INTERGENIC) 730 NON_CODING TCTGTGACAGTTGGTATTGTCAGTCTTTCACTAGAGATTTCAATGAG (INTRONIC) TTAAACATAAGCGACACTCAGTTCATTATTCTTAGTAATGAGGGAT GAAGACAGGACATAAGCAAAGTGAATAACAAAAATAGAAATTTTA TCCACAAAAAATCAATACCTCCTTTGCTCAGCTAATGTGCAATAGT GATAGTCTAGACAAATTAAAGAAATTCCATTTTATTTTAAACACTC TAGTTACTTTTGTGTAGTCTAACATATTGTACATATTAGGTACTCAC TAAATCTCCTTTGATTGGTTTCCTTAGCCTTACTCTGAGATGTTTTAT TCAGTTAACAAATGCTTACATAATGCTTGCAGTGAGC 731 NON_CODING GACAGATCTTCTTGTGTTTAGTGAA (INTRONIC) 732 NON_CODING TAGGATAATTGGTTCTAGAATTGAATTCAAAAGT (UTR) 733 NON_CODING TTTTGGTAAGTGCTCAGGCAACCTG (INTERGENIC) 734 NON_CODING ATTGCATGAACACATATTTGCTGCCAGAAATAATTATTACATTGCC (ncTRANSCRIPT) TTCTTCATATTGAAAACTAACAGTTCTTAAAAGGGAAGCAGAGGTG TTAAAGAGCTTGGTTACAATTTATTGCTAAGAGTTTGGACTTTACAT TAGGAAGATAGCCTCTGAAATACAACG 735 NON_CODING AATAATAATATTTAGGCATGAGCTCTT (UTR) 736 NON_CODING TGGTAATACGGGACTTTATTTGTGA (INTRONIC) 737 NON_CODING TAAGTAGGGAGTGGACTCCCTTCTC (ncTRANSCRIPT) 738 NON_CODING TGCCCTCTATAAACTTCGGACTGTGCACTCACATTAACAGTGTGTA (INTRONIC) AAAGGACTTGTTTCTTGTACACATTTGGCTAACATTAACTATACTAA ATCTTTTCAAGCACCTGATGTAGTTTCTTTAATTATAGGTAGATTTG GACATTTTTTGGATACATTTCGTGGCTGTTTAACTTCTTTCCTTTAA ATTGACTGAATGGCTTTGTCCATTTTTCTATTGAGTCATTTCATTTTT TTTCTGATTTGTTTGGATTTCTTTTTGTATAATTTATATTTTCCCTGG ATAGTTGCAAGAAATTGTTAATAAATTGTTCTCCCTGGCTCCTTTCC TGTGGTATATCCCTGGTTCCCATGTCGTTATCTCTCCTTACTGTCCTC ATTTCGAAGGCACACTTTC 739 NON_CODING GCAACACCTCTTCCTCTTATTGAAA (INTRONIC_ ANTISENSE) 740 NON_CODING GTAATTCGTATGCAAGAAGCTACAC (UTR) 741 NON_CODING ATTTAGGGATTAGTTACAGTTATGCTGTTTCGTAAAATTGGCATTTG (INTRONIC) ATTCTATATTTTATGCATAGATTTTTTTTAAAAGCACTCTTCTGTAG AATTGCACTTAGACCA 742 NON_CODING GCCTTCTTGATCTGGAAGTCAGAGG (INTRONIC) 743 NON_CODING TTTAGCATGAACTGGTGTTGAAATT (INTERGENIC) 744 NON_CODING AGATGAGCTGCTCAGACTCTACAGCATGACGACTACAATTTCTTTT (UTR) CATAAAACTTCTTCTCTTCTTGGAATTATTAATTCCTATCTGCTTCCT AGCTGATAAAGCTTAGAAAAGGCAGTTATTCCTTCTTTCCAACCAG CTTTGCTCGAGTTAGAA 745 NON_CODING ACTTTACAGTCAGAATCAGACCACT (INTRONIC) 746 NON_CODING TGAGGACCTTGGTAATGTTTCTTCCTG (CDS_ANTISENSE) 747 NON_CODING TTGCTTTGGTGGAATATGTATGCTA (ncTRANSCRIPT) 748 NON_CODING TCACAACTCTATAAACCCAACCGAA (INTERGENIC) 749 NON_CODING AGATGAAACAACTGAGGGCCAAAAA (CDS_ANTISENSE) 750 NON_CODING GAGAATGAACTCCACCACTTACGAA (ncTRANSCRIPT) 751 NON_CODING ATGTCAGCTCCTTGTTTACCAATAA (INTRONIC_ ANTISENSE) 752 NON_CODING ACAACTATCTTAACTGCAAAACTTGTGTTCT (INTRONIC) 753 NON_CODING ATGGGAGTAGGAAAGCTAATCAAAAA (INTRONIC) 754 NON_CODING TAAATCTATAATATGGCTGGAGGCA (UTR) 755 NON_CODING GCTTCTCTCCAGACTTGGGCTTAAG (UTR) 756 NON_CODING AAAAGAAGAGTAGTCCAAGGTGTGG (ncTRANSCRIPT) 757 NON_CODING TTACTTAGTCTTCTATGTATAGCTATCAAGGA (UTR) 758 NON_CODING ATGCTGCAAAATGTACCAGTACCTG (INTRONIC) 759 NON_CODING ATGACTCTGACTAGCCAGCAGGAAG (INTERGENIC) 760 NON_CODING GCTGTCCTTTGTGTCAGCATCATGA (INTRONIC) 761 NON_CODING AAGTGAAGTTTGAAGTCTGCTCTCTGCAAAGAGGGTGGGAGTGGGT (INTRONIC) GGAGAAGAGGCTTGTTTTAAAAGCCAAAAACAGAAAGTAAAAAGA AATGGGAAAGTAAAACCAAAGCAGCAAGTGACTCTCTTCTGATGT GCACTTTTCATTTTTCTCCCCCACATTTCAGTGTTAGAAAGAAAACG AGAGGAGCTAGGGAAAGAAGGAGTTGGGGACAGAAGACTAAGAT TTCAACGTGAAATTCCATTTACAAAGGCTTTACTGCAAACAATAGC TAATTTAGTCCTGTAAACATGCATTTATCATACATTTTAATTTTAAT ATTAAAAATACTGCATGTAAATGTTCTGAACTAAAGGTAGATAGCA ATATGTAGTTTGCCATAAAATGAATGCATGTCTTATTCTTTTCCATA GTTCTTCATTAATGAGACTTGTAGTCAAGAATAGATTGAAGATACC ATTCTCCTTGTGTAGTTCAAAAA 762 NON_CODING GCACAGCACAGCTTGGGTTATCTGG (INTERGENIC) 763 NON_CODING ACCCTGCCCATTGGATGTTAGCTGA (INTERGENIC) 764 NON_CODING AAAATTTTATCATCTGGTCATGGTG (INTRONIC) 765 NON_CODING ATTTGGGACAGCTTTACAATGTTAT (INTRONIC) 766 NON_CODING TCAGGAACCTTTCAAAAATACATGC (INTRONIC) 767 NON_CODING CCCCTACCCTTTGTTCTCAGCAGCAAG (INTERGENIC) 768 NON_CODING GACACTGTGAGCTTGATACTGCTGG (UTR_ANTISENSE) 769 NON_CODING GAAACCAAATGGTGTGCCACAAATTAGGGAACACAAGCAAAC (INTRONIC) 770 NON_CODING GAATGATCCATCTTCCTTAAGGCTGCTACACCATAACTAGGAGCTT (INTERGENIC) TAAAAAAAAGGGGGGGGCATTTACTCTCTGAGGCACTCAAAAAAG CACATGCTTTTAATTGAGGGATGGGGGTGACAATGGATCATTCTGT TGATTTTAACTATCTCATATTTGTTAACAGCATCATTTCCATGGATA GCTTTCTGAAAGACTGCCTATCCACTTAGAGGTGAGGAGAAGTAAT AGGGGAGGAAACCCTGCCGAGCTGCAAAAAG 771 NON_CODING GCCTAGGTGACCCAAAGTAATGGGA (INTRONIC) 772 NON_CODING CCTCCGCGCAATTCAGCTGCAGCTG (INTERGENIC) 773 NON_CODING CCAGCTCCACTGAAACAGGGGAAAT (CDS_ANTISENSE) 774 NON_CODING GGTGCCCTAACCACTTCCTGAAATCTGGCCTGATTTTTAATAGCTTT (INTRONIC) TACCTAAGTTCCTCAGATTCTCTGATTCATAGTTTTCAAAATATCTT GTCTCCTATTTTTGTATATTGTTCTCGGCTTCTTCTGCATTTTAACTC AAGTATAGGCAATTCTCACTATATTTACTGGA 775 NON_CODING TGAATGCCATAGTAGTGAATGAATACT (INTRONIC) 776 NON_CODING CCTATATGGCATCGCAGTCTGCAAA (INTRONIC) 777 NON_CODING GTGGCTCTCAGACTTTACTAATCAT (ncTRANSCRIPT) 778 NON_CODING ACTTGCTATACATAAGATGATTCAC (UTR) 779 NON_CODING GTATGCTTATCTGTTTATCTTAGCCAAA (INTRONIC) 780 NON_CODING ATGCTGAAATACTTCTGCCTTTTAG (INTRONIC) 781 NON_CODING GTACTCATGACTCAACCACAGAAGA (CDS_ANTISENSE) 782 NON_CODING GCAGAAACGATGCAGTGGAGCATCAG (INTRONIC_ ANTISENSE) 783 NON_CODING ATGAATTCGGTTCCGTAAGTTTGAG (INTRONIC) 784 NON_CODING CTGTAAGAGTCAGAGCTTTCTGGGA (INTRONIC) 785 NON_CODING CTTGATGTGACAGAGTAGTGTGTTTTCAT (UTR) 786 NON_CODING CATAAAGAATGCACATGAACAGCAG (INTERGENIC) 787 NON_CODING ATGCTGTACCCCTCGGAGACAAATTCCACCCTCGAGTGCG (INTERGENIC) 788 NON_CODING GCATGTTCAGAATCTTGGATCCCTAAGTTCAATATATTGGACATATT (INTRONIC) TAGGAACTCTGGAAATTATGTTGTTTTCACATATCTAGTAACTTACT AGATGAATCAGTAGATTTCATTAAAGTATATCTAATAACAGATAAT TATGATGTACTTCTGGGTTGACATGCATGTCTCTCATTATCAGCTAT CAGTATTAGTGTCATGCTTTGGAGACAGTTATCTTTTGAAGGTTTTG GGGTTCTTATGAACCTCATTTTTCCCAGGAAGTTTCTGTAATTCCTC CTATGCCTATTCTTGTCTTTTCTGTCTGCTTGCAGTGTAAGTTATTTA GATCAGAGGCAATTATTTTTCAGGAAGAAAGAAATCATCAAGTGA CACTCCTAAAGGCAGTA 789 NON_CODING TTTGAAACAGGTGACTCTAGCCATG (INTERGENIC) 790 NON_CODING GGATGTTCGGAGACCATTTTTCCAA (INTRONIC) 791 NON_CODING TTCTGCTTCTGCTATAGGAGAGTGA (INTRONIC) 792 NON_CODING TGCATGTGCTTGTTGATACTCCGCA (INTERGENIC) 793 NON_CODING ATAAAACTGTCAGGCCCAAATAAAT (INTERGENIC) 794 NON_CODING GACTTTGAGACAAGCTTAGGCATCA (INTRONIC) 795 NON_CODING CTCCTCTGGCCTCTAATAGTCAATGATTGTGTAGCCATGCCTATCAG (UTR) TAAAAAGA 796 NON_CODING GAATCAAAACAGACGAGCAAAAAGA (CDS_ANTISENSE) 797 NON_CODING TTGAAGCCAGCCTGAACAATGGCAG (ncTRANSCRIPT) 798 NON_CODING ATCTCTGGGGTGTTACAGAGACAAA (INTRONIC) 799 NON_CODING GATATTCAGAATTCAATTGCCAAGTGCCAAA (INTRONIC) 800 NON_CODING ATTTGCATCTTTAAGTTCTACATTCACTTC (INTRONIC) 801 NON_CODING AGAACTTCAGCCAAAGCATCTGAGA (UTR) 802 NON_CODING CTCAGGATCCCAACCTTTATGTATCAGTTTGCCCTCTTGTTGAATAT (INTRONIC) ATTTACTGTCCAGTGCTACTCCCTCTATCTGTGTGAAAAAATTATTT CAAATTTCCACATCAGGAAAACATCCATGAATGCTTGCCAAGACAA CCGGGAAAAAAACAGTAAGGTCATATTCATGACTGTAAAACCCTTG TTTC 803 NON_CODING TTCAAGTAGACCTAGAAGAGAGTTTTAAAAAACAAAACAATGTAA (UTR) GTAAAGGATATTTCTGAATCTTAAAATTCATCCCATGTGTGATCAT AAACTCATAAAAATAATTTTAAGATGTCGGAAAAGGATACTTTGAT TAAATAAAAACACTCATGGATATGTAAAAACTGTCAAGATTAAAAT TTAATAGTTTCATTTATTTGTTATTTTATTTGTAAGAAATAGTGATG AACAAAGATCCTTTTTCATACTGATACCTGGTTGTATATTATTTGAT GCAACAGTTTTCTGAAATGATATTTCAAATTGCATCAAGAAATTAA AATCATCTATCTGAGTAGTCAAAATACAAG 804 NON_CODING TTATGTCAAAACATTTCCAGAGACT (INTRONIC) 805 NON_CODING GCAAAGCAGTTTAGCAATGACCAGATGTAATTCATTTTGGAGTTCT (INTRONIC) AAGTTTGAACTTAATCAATATGAACTTACAGCCATGGAAGAAGTGA TTATCATTTGTTATTTGCTGGCACAAGAA 806 NON_CODING GGGATAGTGAGGCATCGCAATGTAAGACTCGGGATTAGTACACAC (UTR) TTGTTGATTAATGGAAA 807 NON_CODING TGTCACCTCTTAGTACAAAGCCATGCCAGACACTGCACCTACTCTG (INTRONIC) CACTCTAATGAGAACAATCCGGAAAGGATGATTTTCAAGGGAGAG TGACCTCTTCCTGGAGATCTGAGGTTATGTTACAGTATTGTGGAGTT TTGTTGCTTAAAATTCTCCTCCTGTCCTCACAGGCAATTTTGCTAGA GTTGCAATCCTCACATTTG 808 NON_CODING GATCCAGCAATTACAACGGAGTCAAAAATTAAACCGGACCATCTCT (UTR) CCAACT 809 NON_CODING TGCCAAGGAGGCGTATTCTTCAATATTTGGAATAGACGTGTTCTC (UTR) 810 NON GTGCATACATTATGATACAGCCCTGATCTTTAAAAGGAGCAAAAAT (INTRONIC_ CAGAGAATCGTATGTCTTAAAGAACTATTTCCTTACTTTTTTATGCT ANTISENSE) AGGTAATGCCCATGTGACAAACATGTAAATATTCATCAAAGACCAC ATGTATATATTTTAAAGGCATTTTTTCTTCTCCCCAACTGTATGTAT AGCTAGAATCTGCTTG 811 NON_CODING ATTCTTTACTGAACTGTGATTTGACATT (INTRONIC) 812 NON_CODING GTTAGTGATATTAACAGCGAAAAGAGATTTTTGT (INTRONIC) 813 NON_CODING TTAAGTGAGGCATCTCAATTGCAAGATTTTCTCTGCATCGGTCAG (INTRONIC) 814 NON_CODING CTTCATGCTTAATACAAACACTTCTAATGGCTCATTGATTATAATGT (INTRONIC) ATTATCACATTTTATTTTATCCTCAGACATGATTGACTTTCTAAAGG CTTGAATCAAA 815 NON_CODING ATGGCAGGATTCAACATCTATTTGCTTTATAAGATATTGATAAAAA (INTRONIC_ TGTATCTCATTCATAATGGTGTAGCAACTACTTTTTAATGGGGTTTT ANTISENSE) ACTATGCTCTTTTGTTTCCATTGGCTTTATAAATTAGGATTTGACTTT GCTTTAATTACATGTTTTTAATTACCCAGTTATCTAGTTATCAAATG AAAATGTTATTACTAATATAATTGGAACTCATAAAATGCTTAGCTG 816 NON_CODING TTTCCTTATTTCATGATTGTGGCCATT (INTRONIC) 817 NON_CODING TTATGCAGATAAAACCTCCAGGTAGCAGGCTTCAGAGAGAATAGA (INTRONIC) TTATAAATGTTTCTTAGCAGACTTAAAAAGGTGCCAGAAGATCAGG GAAAAGACCTGGAAAGGGAAAGGGAATCTCTATAGAATGTCAATT ATCCTCACAAGAGATAGCTTTGTAGGGCCATTTCAAAATATATCAA AGGAATATATTTTAGGGTAAAATACTTCAGTTTCTTTCAGGGCCTTC TATGTGCCATATGATGCTGTACTAAAGTAAGGCTGGAATTT 818 NON_CODING CTTCTGTTATCTCTTATTCCAGAGAAAAATCTGCTGTCACTAGATTA (INTRONIC) AATGCACTTTTTGAGTTGTCCTAATGACATCAGTTTGGTTTTCATTT TGAAAGAATTAGGGCATCTGACATTTCAGCCTTATCATAGTCCATT TTCAATT 819 NON_CODING TGAGGTGGCTTTGCCATTTTATACCCATAATTAAATAAAAGGGCAA (INTRONIC_ AATCCCCCCTGATAAATACCATGTTTATCATGGCACATAAAACTTT ANTISENSE) ATGGCAGAAAGCCAAGGCCAATTGACATATATATTTAAAGGTACC ATGGAAAGTAAATGCTAACTCTGAATTTAAAACAGTGGGAAGATG ATTAGTAAGAGTTGGTTTCTTGAAAAGGAATTGTTCTGGTAATAGT CATCTTTAATGACTTCCACGGATTATTCAGTGTTTCTTTAGGGATAT GCATAGGACACTGGTGCTTCAGTAGAAACCCCAGTTTTGGTGTATT AAAGATACATCCATTCTTGACTGATCTTTAATCTAGAGTGTGGTTTT AGCCAAGTCTTTGAATCTCATTTAGTC 820 NON_CODING TTTAAGGTGAAATCTCTAATATTTATAAAAGTAGCAAAATAAATGC (UTR) ATAATTAAAATATATTTGGACATAACAGACTTGGAAGCAGATGATA CAGACTTCTTTTTTTCATAATCAGGTTAGTGTAAGAAATTGCCATTT GAAACAATCCATTTTGTAACTGAACCTTATGAAATATATGTATTTC ATGGTACGTATTCTC 821 NON_CODING TCACTGTGTAGAGAACATATATGCATAAACATAGGTCAATTATATG (UTR) TCTCCATTAGAA 822 NON_CODING GCAACTTTTCCGTCAATCAAAAATGATTCTG (INTRONIC) 823 NON_CODING GGTAAAGGATAGACTCACATTTACAAGTAGTGAAGGTCCAAGAGT (UTR) TCTAAATACAGGAAATTTCTTAGGAACTCA 824 NON_CODING CCTACCTCAGAGCTTCACATATATATATGAAAAAAAAAGTGCTTCA (INTRONIC) AATAACTAATAAGTTTAGGAAGTAGGCCTATCCTAAAGCACAAAA ATATTTTATTTATGAGTAAAAAATATTTTTATAAGTACATAATTATT TCAACAATATGTTACTTTTGTCATTTTTCCTACATATTCTTTTATATA TTTTGAACTGTAGACATGTAGCATATTCTAGCACATTGCAGTAATG ACAACT 825 NON_CODING AAGGAAGATATTACTCTCATAATTCCATACTGGTGGAAACCTATCT (INTRONIC) GAGAATGTCTATTTCATTAATCCTCTTGAGTATGTTC 826 NON_CODING TATTCTTAGGGCTTTTGTGTATGTCTGACTTGTTTTTAAATAACTTCC (UTR) TCAGCAATGCAGACCTTAATTTTTATATTTTTTTAAAGTAGCTAACA TAGCAGTAGGCACTTAAGCATTTAGTCAATGATATTGGTAGAAATA GTAAAATACATCCTTTAAATATATATCTAAGCATATATTTTAAAAG GAGCAAAAATAAAACCAAAGTGTTAGTAAATTTTGATTTATTAGAT ATTTTAGAAAAATAATAGAATTCTGAAGTTTTAAAAATGTCAGTAA TTAATTTATTTTCATTTTCAGAAATATATGCATGCAGTTATGTTTTA TTTGATTGTTGACTTAGGCTATGTCTGTATACAGTAACCA 827 NON_CODING GAATATCACTACCTCAGGTTACGGTACACAGGCTATAATTGATGAT (UTR) GATG 828 NON_CODING TCCTGTCCCTTGACCTTAACTCTGATGGTTCTTCAC (UTR) 829 NON_CODING TGGCGCCACTATACTGCTAAACCTATGCATGAAGGTAGTGACTAGG (UTR_ANTISENSE) ATGGAAATCTGTCAGTGCTACAAAAATATGTATGAACAAAATAATT TTCACCCTTTGATAAAGCTACAAGATATAAAATTTAGAATACTTAT ATAATTTCATACTAGATATGTGAAAAATATGCCATGCTAGAACCAT CTTGTT 830 NON_CODING CATTGAGAGATACAAAGCGTTTTCTAGAGAGTGTTTCT (ncTRANSCRIPT) 831 NON_CODING GTGACTATAGAGGCTAACAAGAATGGA (ncTRANSCRIPT) 832 NON_CODING GAGGCAGCCCTTTCTTATGCAGAAAATACAATACGCACTGCATGAG (UTR) AAGCTTGAGAGTGGATTCTAATCCAGGTCTGTCGACCTTGGATATC ATGCATGTGGGAAGGTGGGTGTGGTGAGAAAAGTTTTAAGGCAAG AGTAGATGGCCATGTTCAACTTTACAAAATTTCTTGGAAAACTGGC AGTATTTTGAACTGCATCTTCTTTGGTACCGGAACCTGCAGAAACA GTGTGAGAAATTAAGTCCTGGTTCACTGCGCAGTAGCAAAGATGGT C 833 NON_CODING GCTCCCATTTTTTGCACTGGAATTACTTGCCAAATGGCCTTTTCACC (INTRONIC) ATCTGAAATAGTTAATGTATTCACTTCTTAAATGAGCAAAAGTCTT CAAACTATTAAGAAAGAGCCATAGACTGAGTGCAGGCACCAGTGT GCTCTTATTACTGTGTCAATTAAATGAATGTATTTGAATGTTTGGAT ACTTACCTCTGAATG 834 NON_CODING CCTCTTACACATGACAAGTTTTGGCTTGTTGGTTTTTCAGAAGCGAA (INTRONIC) GAAATATGGCATTGAAAATGATGCTGAGTGTGAAGAAATGTAGAG GACTCATTTTTGATCCCCCAGGGAGACCTATTTTTACTATAAATTTA CTCCAATAATGAGATGTGTAGGAGGATTTACCATTACATAGTTTTA ATACATTTCAGCGTCATTGGAGACTAAACATTTTCTTTCAGAGTAA CTGATAGTTTCTAGCTACCTAAATAAGGATCTTTTCTAAATCTGACA AGAAATTTTGAAAGTTTTTTCACAATGGCATTCTAGAGTCATCTCTA GAATGATGATATTAGATATTAATCATTATTTTATAAAGAGAAGACT TAATGAATACATCTGATGAATGCATTGGTTATAAGGCTAATAGTTT TACATATAAGCTAGAAACAAAATGAGTCTGTTTGTGAAATTATCTC CTCTACTCTAGTGGAAGAATCTGTAGTGAGATTACTAATAAAGGAC TAATGTTTTATCATTTGATTTGTTCAGATGGGTAATGCAAAAAAAA CTTTAGCCTTCTGTGAAGTAACCTTAGGA 835 NON_CODING GTAAACAGATGTAATTAGAGACATTGGCTCTTTGTTTAGGCC (UTR) 836 NON_CODING TGAGGGTATCAGAACCAATACTGGAC (UTR) 837 NON_CODING CCCTGTAAAACCCTTGGCTTCTATGAAGGCCATTGAATAACTGCGA (INTRONIC) TATGCCTGTGAAAAATCACAAAAGGTGCAAAGTCCCCTCGCAATAA AGATCAGTCACGATGAGATTTGCACCAATTGAACTTTTAAGATTGT AAAATATTTTGTCTTGCAGAGCTGATGCATATCCATTAAAAAGTAT ATCTTAGTGAGCCTTATCTTCAAGTTAGCAGCGAGAAGAGTAACAA AAACGTGCCAATTTAAAATACTGAAATTCTGGGAAAATGTTTTACT TATGAGTATTTCTTAGTATTGGGCTAGTGTGATAAAGATGGCAGCA TGTTTTGATATCTACTCAGAAATTCATTTCACAAACGAAGATGTTTT AGAGTTGGTGAACATACCTGGCCCATTACTGACAAAACCAATTACC GTATTTATTGGTAATAGAGCTGTTTACAGGATGCTCACTGTAAAAA GAAAGAGAAAGAAGAAAAAAAATCCTGCTTTTT TTTTTTTATCTCTCTCTCTTTTGAAACAAGAGAACAATCCCATTCAC ACATAGTAGCTGCCTTCTTTG 838 NON_CODING GATCCTGCTATGATTCTTCACTGGGGGGAAAGAAGATACATTTAGA (INTRONIC) AAATTGGTTATCTCAGATTCTTAGTATGGTTTTAGTTAGTTAGTTTT ACCACTTGGTAGAGTTAATGATTTGACAAATGACATTTGCTTCTTAT TATCAGCCAGTTGGTTGCTAGCTTTAAAGA 839 NON_CODING ACATATTTTCAAGTTGAATGTCTTCTGTTAATTTCTCTTTATTTTGTT (INTRONIC) TGCCAGTGAATATAGAACCTCTTTT 840 NON_CODING CTTTTGAATTACAGAGATATAAATGAAGTATTATCTGTAAAAATTG (UTR) TTATAATTAGAGTTGTGATACAGAGTATATTTCCATTCAGACAATA TATCATAAC 841 NON_CODING TTTAGATGTTTAACTTGAACTGTTCTGAATT (INTRONIC) 842 NON_CODING TTCAATATTAGCAAGACAGCATGCCTTCAAATCAATCTGTAAAACT (UTR) AAGAAACTTAAATTTTAGTTCTTACTGCTTAATTCAAATAATAATTA GTAAGCTAGCAAATAGTAATCTGTAAGCATAAGCTTATGCTTAAAT TCAAGT 843 NON_CODING CATTGCTGTAATCTAGTGAGGCATCTTGGACTTCTG (ncTRANSCRIPT) 844 NON_CODING TATATGCATCCTTTGACTTTGAATGGCTGCCATAATTGTTTACTGAG (INTERGENIC) 845 NON_CODING TGTCAAACAATGTGTAACTCCAGTTATACAAACATTACTGTATCTC (ncTRANSCRIPT) ATTGGGGATACGAAGCTCTACACACTTGAAGATGGTG 846 NON_CODING GTCCAGACTTGGAGTACAAGTAATAAGAAGAATAAAACTTAATCC (ncTRANSCRIPT) CTTAAGTAGATTCACCATAAGTTAGCTCAGAGCAATTCCAGTGCAA GTATGGTCTGTGATCC 847 NON_CODING GCATTGGATTTACTAGACGAAAACCATACCTCTCTTCAATCAAAAT (UTR) GAAAACAAAGCAAATGAATACTGGACAGTCTTAACAATTTTATAA GTTATAAAATGACTTTAGAGCACCCTCCTTCATTACTTTTGCAAAAA CATACTGACTCAGGGCTCTTTTTTTCTTTTTGCATATGACAACTGTT ACTAGAAATACAGGCTACTGGTTTTGCATAGATCATTCATCTTAATT TTGGTACCAGTTAAAAATACAAATGTACTATATTGTAGTCATTTTA AAGTACACAAAGGGCACAATCAAAATGAGATGCACTCATTTAAAT CTGCATTCAGTGAATGTATTGGGAGAAAAATAGGTCTTGCAGGTTT CCTTTTGAATTTTAAGTATCATAAATATTTTTAAAGTAAATAATACG GGGTGTCAGTAATATCTGCAGAATGAATGCAGTCTTTCATGCTAAT GAGTTAGTCTGGAAAAATAAAGTCTTATTTTCTATGTTTTATTCATA GAAATGGAGTATTAATTTTTAATATTTTCACCATATGTGATAACAA AGGATCTTTCATGAATGTCCAAGGGTAAGTCAGTATTAATTAATGC TGTATTACAAGGCAATGCTACCTTCTTTATTCCCCCTTTGAACTACC TTTGAAGTCACTATGAGCACATGGATAGAAATTTAACTTTTTTTTGT AAAGCAAGCTTAAAATGTTTATGTATACATACCCAGCAACTTTTAT AAATGTGTTAAACAATTTTACTGATTTTTATAATAAATATTTTGGTA AGATTTTGAATAATATGAATTCAGGCAGATATACTAAACTGCTTTT ATTTACTTGTTTAGAAAATTGTATATATATGTTTGTGTATCCTAACA GCTGCTATGAA 848 NON_CODING GCTTTGTAAATCAAACTGTGGACTAAATA (INTRONIC) 849 NON_CODING GCTGCTCTTCATTTGATTTCGAGGCAAG (INTRONIC) 850 NON_CODING TCTAGAAGGATTTATTGGCTTCATCAGACATAGGCTAGGATTCTCA (INTRONIC) CGGG 851 NON_CODING AAGTGGCAGTACAACTGAGTATGGTG (INTRONIC) 852 NON_CODING CCATGGATTAGAAGCATTAGTTCTCAGTACTTGAAGACAAACTTCT (CDS_ANTISENSE) AAAAAGAAAATATATGCTCTGAACATCTGAAATGGGCTAGACTTTC AAGTAAAATTGCTTCATTTCTCATTAACTGAAGAGCTATTGATCCA AGTCATACTTGCCATTTAATGTAAATTATTTTTAAACTTTGCTGTAC AAAACCATTAAGTG 853 NON_CODING GAAAAAGGGGTATCAGTCTAATCTCATGGAGAAAAACTACTTGCA (UTR) AAAACTTCTTAAGAAGATGTCTTTTATTGTCTACAATGATTTCTAGT CTTTAAAAACTGTGTTTGAGATTTGTTTTTAGGTTGGTCGCTAATGA TGGCTGTATCTCCCTTCACTGTCTCTTCCTACATTACCACTACTACA TGCTGGCAAAGGTG 854 NON_CODING GATTGAAAGCCAGCTATTTGGTAATGTTTG (INTRONIC) 855 NON_CODING TTTTATGACCTAACAGCACAGATTGTGTT (INTRONIC) 856 NON_CODING TCATCTTTGCCTAAACAGAGATTCT (INTRONIC) 857 NON_CODING TCTGTAACAGTGATTCTCTTGGGTCATATAAAGGACTGAGTTATGG (INTRONIC) AGTTACCTACCCTCTTCGACTCATCTTTTAATTTGTCATAGAAAAAC AACTGTTGTACATTGTGTTAAAAGTTAAATTCTATGGCCAGAGTGT GATTTGGAAAAGAAAACTGAAGTAAGTTGGAAGCAGAGTGAAGAA AATAACTCTGCCATTTTCTTCCAACTCACCCTACAGCATCTCTGTTT TCCAGCCTCACTGGGTTAAGTCTTCAAATGTAGCCCTTTGCTTCTAA GACAATCCCATGTTACAAAGCATCAATAATCCTCCTCTGAACATTT TCCTCAAAAGTTCTAACTACAAAGCAGTTAGCCCTGATGTTCTGAT AAAAGTCTAA 858 NON_CODING CCTTAAGCTGCTCGATTTCTTAAAG (INTRONIC) 859 NON_CODING TGGTTACCAAAGGCAACAGTTGTTATCCAGTGGG (INTERGENIC) 860 NON_CODING TGGGTATCAGTGGATACACACGATGCAACAA (INTERGENIC) 861 NON_CODING AGAGAGGCAACACTTATTATCCACAGGGTAACAGTGGTTACCAGC (INTERGENIC) GATGCAATACTTATTATCCACCGGGTAACGGTGGTTACCAATGAGA CAT 862 NON_CODING GGCAACAACTATTATCCACCGTGTA (INTERGENIC) 863 NON_CODING GTACCATTGATTACCCATGAGACAATGCTTATTTTCCCCCGGGGAA (INTERGENIC) CAGTGGTTACCCTAGAGGCAATACTTATTATCCACAGGGTAACAGT GATAACCCTAGAGGCAATACTTATTATCCACTGGGTAACAGTGGTT ACCGACAAGGCAACACTTATTATCCAAAGGGCAACAGTGGTTACCC AGGAGGAAACAGGTATTATCCACCG 864 NON_CODING ACAGTCGTTATCTATGAGGCAGTACTTATTATCCACCTGGTTACAGT (INTERGENIC) GGTTACCTGGGAGGCAATGCTTATTATCCACCGGGTAACAGTGGTT ACCCTCAAGGCAACAAGTATTATCCACCAGGTAACAGTGGTTACCC TAGAGGCAACACTAATTATCCATTGGGTAACAGTGGTTACTCGCAA GGCAACAATTATTATCCAGCAGGTAACAGTGGATACATGCGATGCA ACAATTATTATCCACCGGGTAACAGTGCTTACCCGTGAGGCAACAC TTATTATCCACGGGGTAACATTGATTACCCACAAGGCAATACTTAC TATCCTCTGGGTAACAGTGCTTTC 865 NON_CODING TAAACCAGGTATCAGTGGTTATGCATGAGGCGACACTTATTATTCA (INTERGENIC) C 866 NON_CODING TAACATTTAGTATCCACTGGGTAAC (INTERGENIC) 867 NON_CODING TTACCCATGAGGCAGCAAATATTATTC (INTERGENIC) 868 NON_CODING TATCCACTGGGTCACAGTGCTTTTCCACGAGAGAATACTTATTATCC (INTERGENIC) AATGGGTAACAGTGGTTACCCATAAGTCGATACATATTATCCACCA G 869 NON_CODING TGCGTAACAGTGGTTACCAACAAGACAACACTTATTATCCACTGGG (INTERGENIC) TAACAATGGTTACCCACAAAACGTCACTTATTATCCACAGGGTAAC AGTGGTTACCCACGAGGCAACACTTATTATCCATGCATTAACAGTT GTTAC 870 NON_CODING AGGATCCACTGGGTACCAATGGTTGCCCACGAGGCAATACTTACTA (INTERGENIC) TCCACTGGGTAACACTGGTTTCCCACGAGGCAACACTTTTTATCCA CCAGATAACAGTGGCTACGCACGAGATAACACTTATTTTCCACAGG GTAAGAATTGTTACCCACGACACAGCACTTATTATCAAGTGGGTAA TACTGGTTACGCAAGAGGCAACACTTATTATAAACCGGGGAACAGT GGTTACTCACAAGGCAATACTTATTATCCACAGGGTAACAGTTGTT ACCCACGAGGCAATACTTATTATCCACTGGGTAACAGTGATCACCC TAGAGGCAATACTTATTATCCACTGGGAAACAGTGGTTACCTACGA GGCAACACTTATTATCCACAGGATAACAGTGGTTACCCATGAGGCA ATACTTACTATCCACCAGGTAACAGTGGTTACCCATGAGGCAATAC TTATTATCCACTGGGTAACAGTGACTACCCATGAGGCAACACTTAT TATTGACCAGGTAACAGTGGTTACCCTAGAAGCAATACCTATTATC CAACAGATAACAGTGGTTACCCATGCGGTAATACTTATTATCCAGT GGGTAGCAGTGGTTACCCATAAGACAATCCTTATTATCCTCCGGGT AACAGTGGTGACCAATGAGGCAATACTTAGTATCCACCGGGTACCA ATGGTTACCCACGAGGCAATACTTACTATCCACCAGGTAACACTGG TTTCCCACGAGGCGACACTTAATATCCACCGGGTCACAGTGGTTAC CCATGAGGCAACACTTATTATCCACAGGGTAAGAGTTGTTACCCAC GAGGCAACACTTATTATCCAGCGGGTAACACTGGTTACCCACGAGG CAACACTTATTACAAACTGGATAACAGTGGTTTCCCACGAGGCAAT ACTTATTATGCAGCAGATTACAGTGGTTACCCATGAGGCAATACTT ATTATCCGCCAGGTAAGAGTGGTTACCCATGAGGCAATACTTATTA TCAACTGGGTAACACTGGTTTCCCATGAGGCAACACTTATTATCCA TCGGGTAACCGTGCTTACCCACAAGGCAACACTTATTATCCACATG GTAACAGTGGTTACCAAGGAGGCAATACTTATTACGCATTGGGTAA CAGTGGTTACCCACGAGGCAGTACTTTTTATCCACCGGGTAACAGT GGTTACCCTAGAGGCAACACTTATTATCCATTGGGTAACAGTGGTT ACCCTAAAGGCAACACTTATTATGCACCGGGTAACACCGGTTACCC GTGAGGCAACTATTATTTTCCACTGGGTAACAGTGGTTAGCCACGA GGCAACACGTATTATCCACCGGTTAACAGTGGTTACCCACGAGGCA ACATTTGATATCCAGCAGATA 871 NON_CODING ATCAGGCAAAAGTTAGTATCCAGCGG (INTERGENIC) 872 NON_CODING TTTCCTACGAGGCAATACATATTACCCAATGGGTAACAGTGGTAAC (INTERGENIC) CCACGAGGCAATACGTATTATCCACAGGGTAACAGTGGTTACCTAT GAGGCAATACTTATTATCAACTGGTTAACAGTGGTATCCCATGAAG C 873 NON_CODING CCACGAGGCAATTCTTGTTATCCATAGG (INTERGENIC) 874 NON_CODING GGCCATACATATTATCCACCGGGTGACAGTGGTTACCCAAGAGGCA (INTERGENIC) ATACTTATTATCCATGTGGTAGAAGTGGTTGCCCATGAGGCAATAC TTATTATCCACTGGGTAACAGTGGTTACCCAAGAGGCAATACTTAT TATACACCCAGTAACAGTGGTTACCCACAGTGCAACACTTATTATC CACTGGGTAACTGTGGTTACGCATGAGGCAACTCGTATTACCCACT GGGAAACAGTGGTAACCCACGAGGCAATACGTATTATCCAACAGG TAACAGTGGTTACCCACAAGGCCACACGTATTATCCACTGGGTAAC AGTGGTTACCCACAAGGCAATACTTATTATCCAGTCATTAGAAGTG GTTACCCA 875 NON_CODING ATCAAGTTCACTAAAGCAGGAATGA (INTRONIC) 876 NON_CODING TTCTGGAGGAAACTTGTAATATTGGAGA (INTRONIC) 877 NON_CODING TTTAAGCAACAGTTTGACTGCATACAAAATTCCTGGGTCACATC (INTERGENIC) 878 NON_CODING TTCTCTACTGCAATGCTGAGGTCTCAGTAAATCGATTTTTGTCTGTG (INTERGENIC) CA 879 NON_CODING GAGTGCTCACTCCATAAGACCCTTACATT (ncTRANSCRIPT) 880 NON_CODING TGTGTAACTGCACACGGCCTATCTCATCTGAATAAGGCCTTACTCTC (ncTRANSCRIPT) AGACCCCTTTTGCAGTACAGCAGGGGTGCTGATAACCAAGGCCCAT TTTCCTGGCCTGTTATGTGTGTGATTATATTTGTCCAGGTTTCTGTGT ACTAGACAAGGAAGCCTCCTCTGCCCCATCCCATCTACGCATAATC TTTCTTT 881 NON_CODING GTGCCAGCTCCATAAGAACCTTACATT (ncTRANSCRIPT) 882 NON_CODING CAACCATGCACCTTGGACATAAATGTGTGTAACTGCACATGGCCCA (ncTRANSCRIPT) TCCCATCTGAATAAGGTCCTACTCTCAGACCCCTTTTGCAGTACAGT AGGTGTGCTGATAACCAAGGCCCCTCTTCCTGGCCTGTTAACGTAT GTGATTATATTTGTCTGGGTTCCAGTGTATAAGACATG 883 NON_CODING TGAGCATAGGCACTCACCTTGGACATGAATGTGCATAACTGCACAT (ncTRANSCRIPT) GGCCCATCCCATCTGAATAAGGTCCTACTCTCAGACCCTTTTTGCAG TACAGCAGGGGTGCTGATCACCAAGGCCCCTTTTCCTGGCCTGTTA TGTGTGTGATTATATTTGTTCCAGTTCCTGTGTAATAGACATGG 884 NON_CODING TCCACTCCATATACCCTTACATTTGGACAAT (ncTRANSCRIPT) 885 NON_CODING CCCTCTCCATAAGACGCTTACGTTTGGA (ncTRANSCRIPT) 886 NON_CODING GCACCTTAGACATGGATTTGCATAACTACACACAGCTCAACCTATC (ncTRANSCRIPT) TGAATAAAATCCTACTCTCAGACCCCTTTTGCAGTACAGCAGGGGT GCTGATCACCAAGGCCCTTTTTCCTGGCCTGGTATGCGTGTGATTAT GTTTGTCCCGGTTCCTGTGTATTAGACATG 887 NON_CODING GGAGTGCCCACTCCATAAGACTCTCACATTTG (ncTRANSCRIPT) 888 NON_CODING TTATTTGGAGAGTCTAGGTGCACAAT (ncTRANSCRIPT) 889 NON_CODING TTTCGTTGTATCCTGCCTGCCTAGCATCCAGTTCCTCCCCAGCCCTG (ncTRANSCRIPT) CTCCCAGCAAACCCCTAGTCTAGCCCCAGCCCTACTCCCACCCCGC CCCAGCCCTGCCCCAGCCCCAGTCCCCTAACCCCCCAGCCCTAGCC CCAGTCCCAGTCCTAGTTCCTCAGTCCCGCCCAGCTTCTCTCGAAAG TCACTCTAATTTTCATTGATTCAGTGCTCAAAATAAGTTGTCCATTG CTTATCCTATTATACTGGGATATTCCGTTTACCCTTGGCATTGCTGA TCTTCAGTACTGACTCCTTGACCATTTTCAGTTAATGCATACAATCC CATTTGTCTGTGATCTCAGGACAAAGAATTTCCTTACTCGGTACGTT GAAGTTAGGGAATGTCAATTGAGAGCTTTCTATCAGAGCATTATTG CCCACAATTTGAGTTACTTATCATTTTCTCGATCCCCTGCCCTTAAA GGAGAAACCATTTCTCTGTCATTGCTTCTGTAGTCACAGTCCCAATT TTGAGTAGTGATCTTTTCTTGTGTACTGTGTTGGCCACCTAAAACTC TTTGCATTGAGTAAAATTCTAATTGCCAATAATCCTACCCATTGGAT TAGACAGCACTCTGAACCCCATTTGCATTCAGCAGGGGGTCGCAGA CAACCCGTCTTTTGTTGGACAGTTAAAATGCTCAGTCCCAATTGTCA TAGCTTTGCCTATTAAACAAAGGCACCCTACTGCGCTTTTTGCTGTG CTTCTGGAGAATCCTGCTGTTCTTGGACAATTAAAGAACAAAGTAG TAATTGCTAATTGTCTCACCCATTAATCATGAAGACTACCAGTCGC CCTTGCATTTGCCTTGAGGCAGCGCTGACTACCTGAGATTTAAGAG TTTCTTAAATTATTGAGTAAAATCCCAATTATCCATAGTTCTGTTAG TTACACTATGGCCTTTGCAAACATCTTTGCATAACAGCAGTGGGAC TGACTCATTCTTAGAGCCCCTTCCCTTGGAATATTAATGGATACAAT AGTAATTATTCATGGTTCTGCGTAACAGAGAAGACCCACTTATGTG TATGCCTTTATCATTGCTCCTAGATAGTGTGACTACCTACCACCTT GCATTAATATGTAAAACACTAATTGCCCATAGTCCCACTCATTAGT CTAGGATGTCCTCTTTGCCATTGCTGCTGAGTTCTGACTACCCAAGT TTCCTTCTCTTAAACAGTTGATATGCATAATTGCATATATTCATGGT TCTGTGCAATAAAAATGGATTCTCACCCCATCCCACCTTCTGTGGG ATGTTGCTAACGAGTGCAGATTATTCAATAACAGCTCTTGAACAGT TAATTTGCACAGTTGCAATTGTCCAGAGTCCTGTCCATTAGAAAGG GACTCTGTATCCTATTTGCACGCTACAATGTGGGCTGATCACCCAA GGACTCTTCTTGTGCATTGATGTTCATAATTGTATTTGTCCACGATC TTGTGCACTAACCCTTCCACTCCCTTTGTATTCCAGCAGGGGACCCT TACTACTCAAGACCTCTGTACTAGGACAGTTTATGTGCACAATCCT AATTGATTAGAACTGAGTCTTTTATATCAAGGTCCCTGCATCATCTT TGCTTTACATCAAGAGGGTGCTGGTTACCTAATGCCCCTCCTCCAG AAATTATTGATGTGCAAAATGCAATTTCCCTATCTGCTGTTAGTCTG GGGTCTCATCCCCTCATATTCCTTTTGTCTTACAGCAGGGGGTACTT GGGACTGTTAATGCGCATAATTGCAATTATGGTCTTTTCCATTAAAT TAAGATCCCAACTGCTCACACCCTCTTAGCATTACAGTAGAGGGTG CTAATCACAAGGACATTTCTTTTGTACTGTTAATGTGCTACTTGCAT TTGTCCCTCTTCCTGTGCACTAAAGACCCCACTCACTTCCCTAGTGT TCAGCAGTGGATGACCTCTAGTCAAGACCTTTGCACTAGGATAGTT AATGTGAACCATGGCAACTGATCACAACAATGTCTTTCAGATCAGA TCCATTTTATCCTCCTTGTTTTACAGCAAGGGATATTAATTACCTAT GTTACCTTTCCCTGGGACTATGAATGTGCA 890 NON_CODING GCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGC (ncTRANSCRIPT) CGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTAT CGGATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGC CCATCGGGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGG CCCATCGGGGCTTCGGATACCTGCTTTTTTTTTTTTTATTTTTCCTTG CCCATCGGGGCCTCGGATACCTGCTTTAATTTTTGTTTTTCTGGCCC ATCGGGGCCGCGGATACCTGCTTTGATTTTTTTTTTTCATCGCCCAT CGGTGCTTTTTATGGATGAAAAAATGTTGGTTTTGTGGGTTGTTGCA CTCTCTGGAATATCTACACTTTTTTTTGCTGCTGATCATTTGGTGGT GTGTGAGTGTACCTACCGCTTTGGCAGAGAATGACTCTGCAGTTAA GCTAAGGGCGTGTTCAGATTGTGGAGGAAAAGTGGCCGCCATTTTA GACTTGCCGCATAACTCGGCTTAGGGCTAGTC 891 NON_CODING ATGGTGATTACTTTCTGTGGGGCTCGGAACTACATGCCCTAGGATA (ncTRANSCRIPT) TAAAAATGATGTTATCATTATAGAGTGCTCACAGAAGGAAATGAA GTAATATAGGTGTGAGATCCAGACCAAAAGTCATTTAACAAGTTTA TTCAGTGATGAAAACATGGGACAAATGGACTAATATAAGCGCAGTG TACTAAGCTGAGTAGAGAGATAAAGTCCTGTCCAGAAGATACATG CTTCCTGGCCTGATTGAGGAGATGGAAAATTTTTGCAAAAAACAAG GTGTTGTGGTCTTCCATCCAGTTTCTTAGTGCTGATGATAAAAGTG AATTAGACCCACCTTGACCTGGCCTACAGAAGTAAAGGAGTAAAA ATAAATGCCTCAGGCGTGCTTTTTGATTCATTTGATAAACAAAGCA TCTTTTATGTGGAATATACCATTCTGGGTCCTGAGGATAAGAGAGA TGAGGGCATTAGATCACTGACAGCTGAAGATAGAAGAACATCTTTG GTTTGATTGTTTAAATAATATTTCAATGCCTATTCTCTGCAAGGTAC TATGTTTCGTAAATTAAATAGGTCTGGCCCAGAAGACCCACTCAAT TGCCTTTGAGATTAAAAAAAAAAAAAAAAAGAAAGAAAAATGCAA GTTTCTTTCAAAATAAAGAGACATTTTTCCTAGTTTCAGGAATCCCC CAAATCACTTCCTCATTGGCTTAGTTTAAAGCCAGGAGACTGATAA AAGGGCTCAGGGTTTGTTCTTTAATTCATTAACTAAACATTCTGCTT TTATTACAGTTAAATGGTTCAAGATGTAACAACTAGTTTTAAAGGT ATTTGCTCATTGGTCTGGCTTAGAGACAGGAAGACATATGAGCAAT AAAAAAAAGATTCTTTTGCATTTACCAATTTAGTAAAAATTTATTA AAACTGAATAAAGTGCTGTTCTTAAGTGCTTGAAAGACGTAAACCA AAGTGCACTTTATCTCATTTATCTTATGGTGGAAACACAGGAACAA ATTCTCTAAGAGACTGTGTTTCTTTAGTTGAGAAGAAACTTCATTGA GTAGCTGTGATATGTTCGATACTAAGGAAAAACTAAACAGATCACC TTTGACATGCGTTGTAGAGTGGGAATAAGAGAGGGCTTTTTATTTT TTCGTTCATACGAGTATTGATGAAGATGATACTAAATGCTAAATGA AATATATCTGCTCCAAAAGGCATTTATTCTGACTTGGAGATGCAAC AAAAACACAAAAATGGAATGAAGTGATACTCTTCATCAAACAGAA GTGACTGTTATCTCAACCATTTTGTTAAATCCTAAACAGAAAACAA AAAAAATCATGACGAAAAGACACTTGCTTATTAATTGGCTTGGAAA GTAGAATATAGGAGAAAGGTTACTGTTTATTTTTTTTCATGTATTCA TTCATTCTACAAATATATTCGGGTGCCAATAGGTACTTGGTATAAG GTTTTTGGCCCCAGAGACATGGGAAAAAAATGCATGCCTTCCCAGA GAATGCCTAATACTTTCCTTTTGGCTTGTTTTCTTGTTAGGGGCATG GCTTAGTCCCTAAATAACATTGTGTGGTTTAATTCCTACTCCGTATC TCTTCTACCACTCTGGCCACTACGATAAGCAGGTA 892 NON_CODING TGTGAACTCACTGTTAAAGGCACTGAAAATTTATCATATTTCATTTA (ncTRANSCRIPT) GCCACAGCCAAAAATAAGGCAATACCTATGTTAGCATTTTGTGAAC TCTAAGGCACCA 893 NON_CODING GGACTAAGCTTGTTGTGGTCACCTATAATGTGCCAGATACCATGCT (ncTRANSCRIPT) GGGTGCTAGAGCTACCAAAGGGGGAAAAGTATTCTCATAGAACAA AAAATTTCAGAAAGGTGCATATTAAAGTGCTTTGTAAACTAAAGCA TGATACAAATGTCAATGGGCTACATATTTATGAATGAATGAATGGA TGAATGAATATTAAGTGCCTCTTACATACCAGCTATTTTGGGTACTG TAAAATACAAGATTAATTCTCCTATGTAATAAGAGGAAAGTTTATC CTCTATACTATTCAGATGTAAGGAATGATATATTGCTTAATTTTAAA CAATCAAGACTTTACTGGTGAGGTTAAGTTAAATTATTACTGATAC ATTTTTCCAGGTAACCAGGAAAGAGCTAGTATGAGGAAATGAAGT AATAGATGTGAGATCCAGACCGAAAGTCACTTAATTCAGCTTGCGA ATGTGCTTTCTA 894 NON_CODING GGGGACAGCCTGAACTCCCTGCTCATAGTAGTGGCCAAATAATTTG (ncTRANSCRIPT) GTGGACTGTGCCAACGCTACTCCTGGGTTTAATACCCATCTCTAGG CTTAAAGATGAGAGAACCTGGGACTGTTGAGCATGTTTAATACTTT CCTTGATTTTTTTCTTCCTGTTTATGTGGGAAGTTGATTTAAATGAC TGATAATGTGTATGAAAGCACTGTAAAACATAAGAGAAAAACCAA TTAGTGTATTGGCAATCATGCAGTTAACATTTGAAAGTGCAGTGTA AATTGTGAAGCATTATGTAAATCAGGGGTCCACAGTTTTTCTGTAA GGGGTCAAATCATAAATACTTTAGACTGTGGGCCATATGGTTTCTG TTACATATTTGTTTTTTAAACAACGTTTTTATAAGGTCAAAATCATT CTTAGTTTTTGAGCCAATTGGATTTGGCCTGCTGTTCATAGCTTA 895 NON_CODING TCTCAAGACTAACGGCCGGAATCTGGAGGCCCATGACCCAGAACC (ncTRANSCRIPT) CAGGAAGGATAGAAGCTTGAAGACCTGGGGAAATCCCAAGATGAG AACCCTAAACCCTACCTCTTTTCTATTGTTTACACTTCTTACTCTTAG ATATTTCCAGTTCTCCTGTTTATCTTTAAGCCTGATTCTTTTGAGATG TACTTTTTGATGTTGCCGGTTACCTTTAGATTGACAGTATTATGCCT GGGCCAGTCTTGAGCCAGCTTTAAATCACAGCTTTTACCTATTTGTT AGGCTATAGTGTTTTGTAAACTTCTGTTTCTATTCACATCTTCTCCA CTTGAGAGAGACACCAAAATCCAGTCAGTATCTAATCTGGCTTTTG TTAACTTCCCTCAGGAGCAGACATTCATATA 896 NON_CODING TGTCTCCTTTTTGGGTCACATGCTGTGTGCTTTTTGTCCTTTTCTTGT (ncTRANSCRIPT) TCTGTCTACCTCTCCTTTCTCTGCCTACCTCTCTTTTCTCTTTGTGAA CTGTGATTATTTGTTACCCCTTCCCCTTCTCGTTCGTTTTAAATTTCA CCTTTTTTCTGAGTCTGGCCTCCTTTCTGCTGTTTCTACTTTTTATCT CACATTTCTCATTTCTGCATTTCCTTTCTGCCTCTCTTGGGCTATTCT CTCTCTCCTCCCCTGCGTGCCTCAGCATCTCTTGCTGTTTGTGATTTT CTATTTCAGTATTAATCTCTGTTGGCTTGTATTTGTTCTCTGCTTCTT CCCTTTCTACTCACCTTTGAGTATTTCAGCCTCTTCATGAATCTATCT CCCTCTCTTTGATTTCATGTAATCTCTCCTTAAATATTTCTTTGCATA TGTGGGCAAGTGTACGTGTGTGTGTGTCATGTGTGGCAGAGGGGCT TCCTAACCCCTGCCTGATAGGTGCAGAACGTCGGCTATCAGAGCAA GCATTGTGGAGCGGTTCCTTATGCCAGGCTGCCATGTGAGATGATC CAAGACCAAAACAAGGCCCTAGACTGCAGTAAAACCCAGAACTCA AGTAGGGCAGAAGGTGGAAGGCTCATATGGATAGAAGGCCCAAAG TATAAGACAGATGGTTTGAGACTTGAGACCCGAGGACTAAGATGG AAAGCCCA 897 NON_CODING TCATTGTTCCTATCTGCCAAATCATTATACTTCCTACAAGCAGTGCA (ncTRANSCRIPT) GAGAGCTGAGTCTTCAGCAGGTCCAAGAAATTTGAACACACTGAA GGAAGTCAGCCTTCCCACCTGAAGATCAACATGCCTGGCACTCTAG CACTTGAGGATA 898 NON_CODING CCTCAGAAGAATAGGCTTGTTGTTTTACAGTGTTAGTGATCCATTCC (ncTRANSCRIPT) CTTTGACGATCCCTAGGTGGAGATGGGGCATGAGGATCCTCCAGGG GAAAAGCTCACTACCACTGGGCAACAACCCTAGGTCAGGAGGTTCT GTCAAGATACTTTCCTGGTCCCAGATAGG 899 NON_CODING CCCATTGAAGATACCACGCTGCATGTGTCCTTAGTAGTCATGTCTCC (ncTRANSCRIPT) TTA 900 NON_CODING AAGAATATTGTTTCTCGGAGAAGGATGTCAAAAGATCGGCCCAGCT (ncTRANSCRIPT) CAGGGAGCAGTTTGCCCTACTAGCTCCTCGGACAGCTGTAAAGAAG AGTCTCTGGCTCTTTAGAATACT 901 NON_CODING GGGTGCCCACTCCTTATGATCTTTACATTTGAACAGTTAATGTGAAT (ncTRANSCRIPT) AATTGCAGTTGTCCACAACCCTATCACTTCTAGGACCATTATACCTC TTTTGCATTACTGTGGGGTATACTGTTTCCCTCCAAGGCCCCTTCTG GTGGACTATCAACATATAATTGAAATTTTCTTTTGTCTTTGTCAGTA GATTAAGGTCATACCCCATCACCTTTCCTTTGTAGTACAACAGGGT GTCCTGATCAACCAAAGTCCTGTTGTTTTGGACTGTTAATATGTGCA ATTACATTTGCTCCTGATCTGTGCACTAGATAAGGATCCTACCTACT TTCTTAGTGTTTTTAGCAGGTAGTGCCCACTACTCAAGACTGTCACT TGGAATGTTCATGTGCACAAACTCAATTCTCTAAGCATGTTCCTGTA CCACCTTTGCTTTAGAGCAGGGGGATGATATTCACTAAGTGCCCCT TCTTTTGGACTTAATATGCATTAATGCAATTGTCCACCTCTTCTTTT AGACTAAGAGTTGATCTCCACATATTCCCCTTGCATCAGGGGCATG TTAATTATGAATGAACCCTTTTCTTTTAATATTAATGTCATAATTGT ATTTGTGGACCTGTGTAGGAGAAAAAGACCCTATGTTCCTCCCATT ACCCTTTGGATTGCTGCTGAGAAGTGTTAACTACTCATAATCTCAG CTCTTGGACAATTAATAGCATTAATAACAATTATCAAGGGCACTGA TCATTAGATAAGACTCCTGCTTCCTCGTTGCTTACATCGGGGGTACT GACCCACTAAGGCCCCTTGTACTGTTAATGTGAATATTTGCAATTAT ATATGTCTCCTTCTGGTAGAGTGGGATATTATGCCCTAGTATCCCCT TTGCATTACTGCAGGGGCTGCTGACTACTCAAAACTTCTCCTGGGA CTGTTAATAGGCACAATGGCAGTTATCAATGGTTTTCTCCCTCCCTG ACCTTGTTAAGCAAGCGCCCCACCCCACCCTTAGTTTCCCATGGCA TAATAAAGTATAAGCATTGGAGTATTCCATGCACTTGTCTATCAAA CAGTGGTCCATACTCCCAACCCTTTTGCATTGCGCCAGTGTGTAAA ATCACAGGTAGCCATGGTGTCATGCTTTATATACGAAGTCTTCCCTC TCTCTGCCCCTTGTGTGCCCTTGGCCCCTTTTTACAGACTATTGCTC ACAATCTCAGGTGTCCATATTTGCAGCTATTAGGTAAGATTGTGCT GTCTCCCTCTTCCCTTCCCTCTGCCCTGCCCCTTTTGCCTCTTTGCTG GGTAATGTTGACCAGACAAGGCCCTTTCTCTTGGACTTAAACAATT CTCAGTTGCACTTTCCTTGGTCCCACCCATTATACATGAACCCCTCT ACTTCCTTTCGCATTGCTTCTGAGTATGCTGACTACCCAAAGCCCCT TCTGTGTTATTAATAAACACAGTACTGATTGTCCCATTTTTCAGCCC ATCAGTCCAAGATCTCCCTACCACTTTGGTGTGTTGGTGCAGTGTTG ACTATGAAAAGCAGGCCTGAACTAGGTGGATAAGCCTTCACTCATT TTCTTTCATTTATTAATGATCCTAGTTTCAATTATTGTCAGATTCTGG GGACAAGAACCATTCTTGCCCACCTGTGTTACTGCTTTACTG 902 NON_CODING TTTGCAGCAAAGTCACCCTTACAAAGAAGCTAATATGGAAACCACA (UTR) TGTAACTTAGCCAGACTATATTGTGTAGCTTCAAGAACTTGCAGTA CATTACCAGCTGTGATTCTCCTGATAATTCAAGGGAGCTCAAAGTC ACAAGAAGAAAAATGAAAGGAAAAAACAGCAGCCCTATTCAGAA ATTGGTTTGAAGATGTAATTGCTCTAGTTTGGATTA 903 NON_CODING ATGGTGGCTGTAAAACTAGGATCCCTGACGATTG (UTR)

TABLE 7 Gene Transcripts Comparison ACPP ACPP-001(protein_coding) PvsM ACPP-005(retained_intron) PvsM NvsM ANK3 ANK3-021(retained_intron) NvsP AR AR-001(protein_coding) NvsM AR-005(nonsense_mediated_decay) PvsM NvsM AR-203 (protein_coding) NvsM CD44 CD44-014(retained_intron) NvsM CHRAC1 CHRAC1-005(retained_intron) NvsM COL1A2 COL1A2-002(retained_intron) NvsM COL1A2-005(retained_intron) NvsM COL1A2-006(retained_intron) NvsM COL1A2-012(retained_intron) NvsM DLGAP1 DLGAP1-008(processed_transcript) PvsM DLGAP1-010(processed_transcript) PvsM DLGAP1-201(protein_coding) PvsM NvsM ETV6 ETV6-002(processed_transcript) NvsM ETV6-003(processed_transcript) PvsM NvsM ETV6-004(protein_coding) NvsM FBLN1 FBLN1-001(protein_coding) PvsM NvsM FBLN1-016(processed_transcript) NvsM FGFR1 FGFR1-005(retained_intron) NvsM FGFR2 FGFR2-008(processed_transcript) ALL FGFR2-016(protein_coding) ALL FGFR2-201(protein_coding) PvsM NvsM ILK ILK-011(processed_transcript) NvsM ILK-012(processed_transcript) NvsM KHDRBS3 KHDRBS3-003(retained_intron) PvsM MYLK MYLK-001(protein_coding) NvsM MYLK-014(retained_intron) NvsM PASK PASK-015(retained_intron) PvsM PDLIM5 PDLIM5-010(protein_coding) PvsM PDLIM5-017(processed_transcript) PvsM NvsM POLR1C POLR1C-002(retained_intron) NvsM ST6GAL1 ST6GAL1-021(retained_intron) PvsM THBS1 THBS1-001(protein_coding) PvsM THBS1-004(processed_transcript) PvsM THBS1-008(retained_intron) PvsM

TABLE 8 Mean Fold Difference Transcript P vs N M vs P M vs N TOP ACOT11-001 0.79 0.77 0.61 AOX1-001 0.79 0.56 0.44 C19orf46-002 1.24* 1.23* 1.53* C8orf84-001 0.76 0.75 0.57 COCH-202 0.76 0.83 0.63 CTA-55110.1-001 0.83 0.68 0.56 DMD-024 0.74 0.82 0.60 FGF10-002 0.83 0.64 0.53 FGFR2-008 0.76 0.79 0.60 FGFR2-016 0.74 0.67 0.49 GABRE-006 0.79 0.83 0.66 GNAL-001 0.82 0.69 0.57 GNAO1-002 0.78 0.75 0.58 HEATR8-006 0.80 0.80 0.64 ISL1-002 0.80 0.81 0.65 NR2F2-202 0.82 0.82 0.68 PCP4-004 0.81 0.72 0.58 PDE5A-005 0.74 0.79 0.59 PDZRN4-202 0.80 0.71 0.57 RSRC2-017 1.27* 1.28* 1.63* TGM4-001 0.68 0.62 0.42 TSPAN2-001 0.80 0.77 0.61 Bottom ABCC4-004 1.35* 0.81 N.A. ALK-001 1.24* 0.83 N.A. ATP1A1-002 1.23* 0.71 N.A. NAMPT-006 1.34* 0.73 N.A. NAMPT-007 1.75* 0.57 N.A. RP11-627G23.1-004 1.38* 0.78 N.A.

TABLE 9 TS-PSRs Genes OR CI OR CI Classifier OR (95%) P-value OR (95%) P-value KNN-positive 13 [2.5-99] <0.005 3.8 [1.0-14.3] 0.05 Nomogram* 6.6 [2.3-20] <0.001 7.9 [2.9-22.6] <0.0001

TABLE 10 Variable Categories N (%) Age <70 yrs 132 (53) ≥70 yrs 119 (47) Gender Male 205 (82) Female  46 (18) Ethnicity Caucasian 222 (88) Other  29 (12) Pathologic Stage T2N0  62 (25) T3N0  75 (30) T4N0  25 (10) Any T N1-3  89 (35) Intravesical therapy No 196 (78) Yes  55 (22) Adjuvant No 150 (60) chemotherapy Yes 101 (40) Age of FFPE blocks <15 yrs 160 (64) ≥15 yrs  91 (36)

TABLE 11 Hazard ratio 95% CI p-value Gender 0.92 0.49-1.71 0.78 Age (<70 vs ≥70) 1.42 0.87-2.30 0.16 Ethnicity 0.89 0.42-1.88 0.75 T stage 2.46 1.05-5.72 0.04 Lymph nodes 3.37 2.07-5.49 <0.001 Lymphovascular invasion (LVI) 1.05 0.97-1.14 0.25 Adjuvant Chemotherapy 0.88 0.72-1.06 0.18

TABLE 12 Variable Parameter Training AUC Testing AUC Gender M/F 0.48 0.56 Age <70/≥70 0.51 0.48 Race Caucasian/Other 0.49 0.54 Tumor Stage 1, 2, 3, 4 0.62 0.66 Node Status Yes/No 0.66 0.65 LVI Yes/No 0.64 0.63 Clinical Classifier 1 Logistic Model 0.73 0.71 Clinical Classifier 2 Cox model 0.72 0.72

TABLE 13 Genomic & Clinicopathologic Hazard Ratio Factors (95% CI) P value GC* 2.20 (1.22-3.92) 0.00841 Age 1.55 (0.34-7.10) 0.58 Ethnicity 0.22 (0.01-3.46) 0.28 Gender 0.69 (0.11-4.40) 0.70 Pathological stage 1.02 (0.32-3.26) 0.97 Lymph node involvement 3.51 (0.76-16.25) 0.11 Lymphovascular invasion 2.90 (0.52-16.07) 0.22 Block age 0.99 (0.80-1.22) 0.93 Intravesical treatment 3.64 (0.64-20.64) 0.14 Adjuvant chemotherapy 4.31 (0.91-20.43) 0.07 *per 0.1 unit increment

TABLE 14 celfile Batch PatientId AdjCTx Age Blockage Gender IV_Rx LNI LVI OS_Event OS_Event_Time AA682-HuEx- 3 1646 0 69 18 male 0 1 1 1 10 1_0-st-v2-01- 1_118.CEL AA629-HuEx- 2 1650 0 59 12 male 1 0 0 0 113 1_0-st-v2-01- 1_132.CEL AA684-HuEx- 3 1652 0 69 19 female 0 0 1 1 19 1_0-st-v2-01- 1_142.CEL AA736-HuEx- 6 1655 1 40 17 male 0 0 1 0 179 1_0-st-v2-02- 2_145.CEL AA685-HuEx- 3 1657 1 57 15 female 0 1 1 1 10 1_0-st-v2-01- 1_157.CEL AA739-HuEx- 6 1662 0 78 8 male 0 1 0 1 2 1_0-st-v2-02- 2_166.CEL AA579-HuEx- 1 1678 0 72 10 male 0 0 1 1 5 1_0-st-v2-01- 1_220.CEL AA636-HuEx- 6 1680 0 76 10 female 0 1 1 1 12 1_0-st-v2-01- 1_226.CEL AA856-HuEx- 5 1691 1 68 10 male 0 1 NA 0 90 1_0-st-v2-01- 1_274.CEL AA746-HuEx- 4 1697 1 49 16 female 0 0 1 1 21 1_0-st-v2-01- 1_292.CEL AA694-HuEx- 3 1698 1 69 13 male 0 0 1 1 77 1_0-st-v2-01- 1_293.CEL AA585-HuEx- 1 1699 0 89 9 male 0 0 NA 1 3 1_0-st-v2-01- 1_294.CEL AA696-HuEx- 3 1702 1 77 9 male 0 1 1 1 9 1_0-st-v2-01- 1_299.CEL AA697-HuEx- 3 1705 1 67 15 male 0 0 1 1 18 1_0-st-v2-01- 1_311.CEL AA750-HuEx- 4 1712 1 68 10 male 0 1 1 0 83 1_0-st-v2-01- 1_343.CEL AA643-HuEx- 6 1716 0 70 19 male 0 0 0 1 24 1_0-st-v2-01- 1_369.CEL AA699-HuEx- 6 1717 1 50 10 male 1 1 1 0 90 1_0-st-v2-01- 1_373.CEL AA753-HuEx- 4 1719 0 66 10 male 0 0 1 0 83 1_0-st-v2-01- 1_376.CEL AA755-HuEx- 4 1723 0 72 10 male 0 0 NA 1 14 1_0-st-v2-01- 1_390.CEL AA798-HuEx- 5 1731 0 70 12 male 1 0 NA 1 20 1_0-st-v2-01- 1_414.CEL AA702-HuEx- 6 1733 0 65 19 male 0 0 1 1 42 1_0-st-v2-01- 1_420.CEL AA704-HuEx- 6 1740 1 74 19 male 0 0 0 1 11 1_0-st-v2-01- 1_444.CEL AA802-HuEx- 5 1747 0 72 14 male 1 0 0 0 130 1_0-st-v2-01- 1_469.CEL AA762-HuEx- 4 1752 1 64 17 male 0 1 1 1 38 1_0-st-v2-01- 1_481.CEL AA763-HuEx- 4 1753 1 48 8 male 1 0 0 0 69 1_0-st-v2-01- 1_485.CEL AA594-HuEx- 1 1754 1 61 15 male 0 1 1 0 155 1_0-st-v2-01- 1_493.CEL AA705-HuEx- 6 1756 1 66 9 male 0 0 0 0 73 1_0-st-v2-01- 1_506.CEL AA597-HuEx- 6 1763 1 68 19 male 0 1 1 1 11 1_0-st-v2-01- 1_529_2.CEL AA805-HuEx- 5 1768 1 58 13 male 0 1 1 1 18 1_0-st-v2-01- 1_560.CEL AA766-HuEx- 4 1769 1 42 20 male 0 1 0 0 203 1_0-st-v2-01- 1_562.CEL AA767-HuEx- 4 1771 1 54 9 female 0 1 1 1 11 1_0-st-v2-01- 1_569.CEL AA806-HuEx- 5 1775 0 64 14 male 0 1 1 1 8 1_0-st-v2-01- 1_594.CEL AA602-HuEx- 6 1785 0 71 17 male 0 0 0 0 169 1_0-st-v2-01- 1_623.CEL AA771-HuEx- 4 1798 0 74 17 male 0 0 0 1 124 1_0-st-v2-01- 1_651.CEL AA772-HuEx- 4 1799 0 48 9 male 0 0 0 0 76 1_0-st-v2-01- 1_652.CEL AA808-HuEx- 5 1801 0 52 13 male 0 0 1 1 6 1_0-st-v2-01- 1_656.CEL AA849-HuEx- 6 1802 0 85 15 male 0 0 0 1 15 1_0-st-v2-01- 1_664.CEL AA774-HuEx- 4 1804 0 81 8 male 0 0 1 0 78 1_0-st-v2-01- 1_666.CEL AA662-HuEx- 6 1814 0 55 14 male 1 0 0 0 143 1_0-st-v2-01- 1_703.CEL AA607-HuEx- 1 1817 0 66 18 male 0 0 0 1 6 1_0-st-v2-01- 1_709.CEL AA719-HuEx- 3 1822 0 72 8 female 0 0 0 0 69 1_0-st-v2-01- 1_726.CEL AA721-HuEx- 3 1832 0 71 18 female 0 0 1 1 20 1_0-st-v2-01- 1_756.CEL AA666-HuEx- 6 1834 1 63 11 male 0 0 NA 1 41 1_0-st-v2-01- 1_763.CEL AA722-HuEx- 3 1837 0 49 16 male 0 0 0 0 159 1_0-st-v2-01- 1_777.CEL AA780-HuEx- 4 1838 1 60 9 male 0 0 0 0 76 1_0-st-v2-01- 1_779.CEL AA781-HuEx- 6 1842 0 47 14 male 0 0 0 1 18 1_0-st-v2-02- 2_800.CEL AA667-HuEx- 6 1848 0 78 12 male 0 0 NA 0 112 1_0-st-v2-01- 1_826.CEL AA619-HuEx- 1 1868 0 67 9 male 0 0 0 1 60 1_0-st-v2-01- 1_881.CEL AA625-HuEx- 1 1887 0 79 13 female 0 1 1 0 120 1_0-st-v2-01- 1_956.CEL AA732-HuEx- 6 1888 0 86 12 male 0 0 1 1 15 1_0-st-v2-01- 1_957.CEL AA680-HuEx- 6 1889 1 56 17 male 0 1 1 1 172 1_0-st-v2-01- 1_958.CEL AA733-HuEx- 6 1890 1 63 16 male 0 1 1 1 8 1_0-st-v2-01- 1_959.CEL AA574-HuEx- 1 1647 0 67 17 male 0 0 0 1 66 1_0-st-v2-01- 1_120.CEL AA628-HuEx- 2 1649 0 65 18 male 0 0 1 1 27 1_0-st-v2-01- 1_130.CEL AA683-HuEx- 3 1651 0 70 9 female 0 0 0 1 11 1_0-st-v2-01- 1_135.CEL AA575-HuEx- 1 1653 0 48 9 female 0 0 NA 1 3 1_0-st-v2-01- 1_143.CEL AA630-HuEx- 2 1654 0 86 13 female 0 0 1 1 73 1_0-st-v2-01- 1_144.CEL AA846-HuEx- 2 1658 0 67 16 male 0 1 1 1 68 1_0-st-v2-01- 1_159.CEL AA576-HuEx- 1 1659 0 68 20 male 0 0 0 1 71 1_0-st-v2-01- 1_162.CEL AA686-HuEx- 3 1661 1 64 17 male 0 0 0 0 149 1_0-st-v2-01- 1_165.CEL AA687-HuEx- 3 1663 0 64 14 male 0 0 0 0 133 1_0-st-v2-01- 1_167.CEL AA631-HuEx- 2 1665 1 52 18 male 0 1 0 1 15 1_0-st-v2-01- 1_173.CEL AA577-HuEx- 1 1667 1 71 10 male 0 1 0 1 15 1_0-st-v2-01- 1_184.CEL AA578-HuEx- 1 1668 0 54 9 male 0 1 1 1 13 1_0-st-v2-01- 1_186.CEL AA632-HuEx- 2 1669 1 50 12 male 0 0 1 1 30 1_0-st-v2-01- 1_195.CEL AA848-HuEx- 3 1670 1 62 12 male 1 0 1 0 107 1_0-st-v2-01- 1_198.CEL AA689-HuEx- 3 1671 0 74 13 male 0 0 1 1 6 1_0-st-v2-01- 1_199.CEL AA633-HuEx- 2 1672 0 83 15 male 1 0 0 1 31 1_0-st-v2-01- 1_203.CEL AA690-HuEx- 3 1673 0 68 14 male 0 0 0 0 108 1_0-st-v2-01- 1_211.CEL AA634-HuEx- 2 1674 0 93 16 male 0 0 1 1 13 1_0-st-v2-01- 1_213.CEL AA691-HuEx- 3 1675 0 74 10 male 0 0 0 1 25 1_0-st-v2-01- 1_214.CEL AA635-HuEx- 2 1676 0 74 19 male 1 1 1 1 78 1_0-st-v2-01- 1_218.CEL AA692-HuEx- 3 1679 0 83 10 male 0 1 1 1 5 1_0-st-v2-01- 1_224.CEL AA580-HuEx- 1 1681 0 58 17 male 0 0 0 1 45 1_0-st-v2-01- 1_227.CEL AA637-HuEx- 2 1682 0 81 15 male 0 0 1 1 7 1_0-st-v2-01- 1_228.CEL AA693-HuEx- 3 1683 0 71 10 male 1 0 1 1 25 1_0-st-v2-01- 1_230.CEL AA581-HuEx- 1 1684 0 78 10 male 0 0 0 0 90 1_0-st-v2-01- 1_235.CEL AA638-HuEx- 2 1688 1 64 15 female 0 1 1 1 55 1_0-st-v2-01- 1_258.CEL AA639-HuEx- 2 1689 1 70 9 male 0 1 1 1 10 1_0-st-v2-01- 1_267.CEL AA582-HuEx- 1 1690 1 57 16 male 1 1 0 1 19 1_0-st-v2-01- 1_272.CEL AA640-HuEx- 2 1694 1 72 10 female 0 1 1 1 18 1_0-st-v2-01- 1_281.CEL AA583-HuEx- 1 1695 1 71 19 male 1 1 1 1 24 1_0-st-v2-01- 1_284.CEL AA584-HuEx- 1 1696 0 61 18 male 0 1 0 1 12 1_0-st-v2-01- 1_286.CEL AA695-HuEx- 3 1700 1 73 9 male 0 1 NA 0 72 1_0-st-v2-01- 1_295.CEL AA586-HuEx- 1 1701 1 71 9 male 0 0 NA 1 32 1_0-st-v2-01- 1_296.CEL AA587-HuEx- 1 1704 0 73 19 male 0 0 0 1 175 1_0-st-v2-01- 1_309.CEL AA641-HuEx- 2 1706 1 66 12 male 0 1 1 1 56 1_0-st-v2-01- 1_314.CEL AA847-HuEx- 2 1709 0 68 11 male 0 0 0 1 29 1_0-st-v2-01- 1_338.CEL AA698-HuEx- 3 1711 0 76 13 male 0 0 1 0 94 1_0-st-v2-01- 1_342.CEL AA642-HuEx- 2 1715 0 44 11 female 0 0 1 1 21 1_0-st-v2-01- 1_368.CEL AA588-HuEx- 2 1718 1 72 12 male 0 1 0 0 112 1_0-st-v2-01- 1_375.CEL AA644-HuEx- 2 1721 0 73 11 male 0 0 0 1 47 1_0-st-v2-01- 1_382.CEL AA700-HuEx- 3 1724 1 78 10 male 1 1 1 1 36 1_0-st-v2-01- 1_393.CEL AA589-HuEx- 1 1725 1 51 13 male 1 0 0 0 126 1_0-st-v2-01- 1_396.CEL AA701-HuEx- 3 1727 1 67 16 male 0 0 0 0 151 1_0-st-v2-01- 1_402.CEL AA590-HuEx- 1 1736 0 78 13 male 0 0 1 1 7 1_0-st-v2-01- 1_430.CEL AA591-HuEx- 1 1737 0 66 12 male 1 0 0 0 111 1_0-st-v2-01- 1_436.CEL AA645-HuEx- 2 1738 1 55 12 female 0 1 0 0 105 1_0-st-v2-01- 1_437.CEL AA703-HuEx- 3 1739 1 67 10 male 0 1 NA 1 50 1_0-st-v2-01- 1_441.CEL AA646-HuEx- 2 1742 1 70 14 male 1 0 0 0 153 1_0-st-v2-01- 1_454.CEL AA592-HuEx- 1 1743 1 68 17 male 0 0 NA 0 170 1_0-st-v2-01- 1_455.CEL AA593-HuEx- 1 1748 0 75 14 female 0 0 0 0 112 1_0-st-v2-01- 1_475.CEL AA647-HuEx- 2 1749 1 74 13 male 0 0 0 0 119 1_0-st-v2-01- 1_476.CEL AA648-HuEx- 2 1750 0 60 14 male 0 0 0 0 132 1_0-st-v2-01- 1_477.CEL AA649-HuEx- 2 1751 0 70 9 female 0 1 1 1 13 1_0-st-v2-01- 1_479.CEL AA650-HuEx- 2 1755 0 81 14 male 1 0 0 1 60 1_0-st-v2-01- 1_504.CEL AA651-HuEx- 2 1758 0 82 10 female 0 0 NA 1 17 1_0-st-v2-01- 1_510.CEL AA595-HuEx- 1 1759 1 67 10 female 0 0 NA 0 97 1_0-st-v2-01- 1_512.CEL AA706-HuEx- 3 1760 0 91 9 female 0 0 1 1 4 1_0-st-v2-01- 1_517.CEL AA596-HuEx- 1 1762 1 47 9 male 0 1 1 0 76 1_0-st-v2-01- 1_528.CEL AA845-HuEx- 2 1764 0 55 9 female 0 0 NA 0 69 1_0-st-v2-01- 1_547.CEL AA598-HuEx- 1 1765 0 77 13 male 1 1 NA 1 4 1_0-st-v2-01- 1_552.CEL AA707-HuEx- 3 1770 1 73 16 male 0 1 1 0 115 1_0-st-v2-01- 1_567.CEL AA599-HuEx- 1 1772 0 67 10 male 0 1 0 1 19 1_0-st-v2-01- 1_579.CEL AA600-HuEx- 1 1773 1 51 8 male 0 0 1 0 66 1_0-st-v2-01- 1_586.CEL AA653-HuEx- 2 1774 0 76 8 male 1 0 0 1 36 1_0-st-v2-01- 1_591.CEL AA654-HuEx- 2 1776 0 57 13 female 0 1 1 1 41 1_0-st-v2-01- 1_596.CEL AA655-HuEx- 2 1777 0 75 19 male 0 0 0 1 128 1_0-st-v2-01- 1_597.CEL AA656-HuEx- 2 1778 0 63 19 male 0 0 0 1 102 1_0-st-v2-01- 1_600.CEL AA657-HuEx- 2 1779 0 78 12 male 1 0 0 0 99 1_0-st-v2-01- 1_608.CEL AA601-HuEx- 1 1780 0 77 17 male 0 1 1 1 13 1_0-st-v2-01- 1_612.CEL AA708-HuEx- 3 1781 0 77 17 male 0 1 1 1 2 1_0-st-v2-01- 1_616.CEL AA709-HuEx- 3 1783 0 86 14 male 1 0 0 1 8 1_0-st-v2-01- 1_619.CEL AA603-HuEx- 1 1786 0 66 14 male 1 0 0 0 127 1_0-st-v2-01- 1_626.CEL AA658-HuEx- 2 1787 1 64 11 male 0 0 0 0 91 1_0-st-v2-01- 1_627.CEL AA659-HuEx- 2 1788 0 74 15 male 0 0 1 1 11 1_0-st-v2-01- 1_630_2.CEL AA604-HuEx- 1 1789 0 72 11 male 1 0 1 1 17 1_0-st-v2-01- 1_640.CEL AA710-HuEx- 3 1791 0 65 12 male 1 0 1 0 107 1_0-st-v2-01- 1_643.CEL AA660-HuEx- 2 1792 0 85 9 male 0 0 NA 1 14 1_0-st-v2-01- 1_644.CEL AA711-HuEx- 3 1793 1 78 10 male 1 1 1 1 5 1_0-st-v2-01- 1_645.CEL AA712-HuEx- 3 1794 1 65 12 female 0 0 0 0 108 1_0-st-v2-01- 1_646.CEL AA713-HuEx- 3 1795 1 61 11 female 1 1 0 1 24 1_0-st-v2-01- 1_647.CEL AA605-HuEx- 1 1796 0 77 17 male 0 0 0 1 69 1_0-st-v2-01- 1_648.CEL AA714-HuEx- 3 1800 0 81 11 male 1 1 1 1 15 1_0-st-v2-01- 1_655.CEL AA716-HuEx- 3 1805 0 67 18 male 0 0 0 0 168 1_0-st-v2-01- 1_668.CEL AA661-HuEx- 2 1806 0 64 17 male 0 0 1 0 172 1_0-st-v2-01- 1_673.CEL AA606-HuEx- 1 1809 0 68 12 male 1 0 NA 0 118 1_0-st-v2-01- 1_686.CEL AA717-HuEx- 3 1811 1 63 9 female 0 1 1 1 10 1_0-st-v2-01- 1_691.CEL AA718-HuEx- 3 1812 1 58 14 male 0 1 1 0 135 1_0-st-v2-01- 1_693.CEL AA663-HuEx- 2 1816 0 74 13 male 0 0 0 1 7 1_0-st-v2-01- 1_708.CEL AA608-HuEx- 1 1820 0 66 11 male 0 0 0 0 104 1_0-st-v2-01- 1_717.CEL AA609-HuEx- 1 1821 0 67 15 female 0 0 0 1 37 1_0-st-v2-01- 1_722.CEL AA610-HuEx- 1 1824 0 83 13 female 0 0 1 1 31 1_0-st-v2-01- 1_734.CEL AA664-HuEx- 2 1825 1 61 8 male 0 1 NA 0 76 1_0-st-v2-01- 1_738.CEL AA611-HuEx- 1 1826 1 69 11 male 1 1 1 1 13 1_0-st-v2-01- 1_740.CEL AA665-HuEx- 2 1827 0 53 9 male 0 0 0 0 50 1_0-st-v2-01- 1_744.CEL AA612-HuEx- 1 1829 1 70 8 male 0 1 1 1 47 1_0-st-v2-01- 1_750.CEL AA720-HuEx- 3 1830 0 63 9 male 1 0 NA 0 87 1_0-st-v2-01- 1_752.CEL AA613-HuEx- 1 1831 0 81 11 female 0 0 0 1 10 1_0-st-v2-01- 1_753.CEL AA614-HuEx- 1 1835 0 49 16 male 0 0 0 0 129 1_0-st-v2-01- 1_767.CEL AA615-HuEx- 1 1839 NA 65 12 male 0 1 1 1 16 1_0-st-v2-01- 1_781.CEL AA723-HuEx- 3 1845 1 52 14 female 0 1 1 1 25 1_0-st-v2-01- 1_816.CEL AA724-HuEx- 3 1847 0 78 18 male 1 0 NA 1 97 1_0-st-v2-01- 1_822.CEL AA668-HuEx- 2 1849 1 77 9 male 1 1 1 1 10 1_0-st-v2-01- 1_827.CEL AA669-HuEx- 2 1850 0 63 17 male 1 0 0 0 160 1_0-st-v2-01- 1_828.CEL AA670-HuEx- 2 1851 1 50 9 male 0 1 1 1 25 1_0-st-v2-01- 1_832.CEL AA616-HuEx- 1 1853 0 75 10 male 1 0 NA 0 90 1_0-st-v2-01- 1_842.CEL AA671-HuEx- 2 1854 1 59 9 male 0 1 1 1 15 1_0-st-v2-01- 1_844.CEL AA725-HuEx- 3 1855 0 75 13 male 0 0 1 0 110 1_0-st-v2-01- 1_846.CEL AA672-HuEx- 2 1857 0 65 12 male 0 0 NA 1 19 1_0-st-v2-01- 1_850.CEL AA617-HuEx- 1 1858 0 54 12 male 0 0 0 0 114 1_0-st-v2-01- 1_852.CEL AA673-HuEx- 2 1860 0 68 10 female 0 0 1 1 3 1_0-st-v2-01- 1_857.CEL AA618-HuEx- 1 1863 1 72 9 male 1 1 1 1 10 1_0-st-v2-01- 1_869.CEL AA726-HuEx- 3 1864 1 61 9 male 1 0 0 0 92 1_0-st-v2-01- 1_872.CEL AA674-HuEx- 2 1866 0 58 18 male 0 0 0 0 175 1_0-st-v2-01- 1_877.CEL AA675-HuEx- 2 1867 1 66 18 male 1 0 0 0 174 1_0-st-v2-01- 1_878.CEL AA727-HuEx- 3 1870 0 73 8 male 0 0 0 1 45 1_0-st-v2-01- 1_892.CEL AA620-HuEx- 1 1871 0 76 15 male 0 0 1 1 22 1_0-st-v2-01- 1_894.CEL AA728-HuEx- 3 1872 1 79 16 male 0 1 1 1 36 1_0-st-v2-01- 1_895.CEL AA621-HuEx- 1 1873 1 66 9 female 0 1 1 1 11 1_0-st-v2-01- 1_902.CEL AA676-HuEx- 2 1874 0 82 7 male 1 0 1 0 47 1_0-st-v2-01- 1_906.CEL AA622-HuEx- 1 1875 1 52 16 male 0 0 0 0 130 1_0-st-v2-01- 1_907.CEL AA677-HuEx- 2 1877 0 81 17 male 0 1 1 1 5 1_0-st-v2-01- 1_911.CEL AA678-HuEx- 2 1878 1 66 20 male 0 1 1 1 32 1_0-st-v2-01- 1_914.CEL AA729-HuEx- 3 1879 0 73 11 female 0 0 0 1 8 1_0-st-v2-01- 1_916.CEL AA623-HuEx- 1 1881 0 85 17 male 0 0 1 1 5 1_0-st-v2-01- 1_924.CEL AA730-HuEx- 3 1883 0 80 16 female 0 0 0 1 61 1_0-st-v2-01- 1_926.CEL AA731-HuEx- 3 1884 1 70 18 male 0 1 1 1 11 1_0-st-v2-01- 1_928.CEL AA679-HuEx- 2 1885 1 68 20 male 0 1 0 1 46 1_0-st-v2-01- 1_932.CEL AA624-HuEx- 1 1886 0 76 9 male 0 0 0 0 76 1_0-st-v2-01- 1_951.CEL AA681-HuEx- 2 1891 1 68 12 female 1 1 1 1 23 1_0-st-v2-01- 1_961.CEL AA626-HuEx- 1 1892 0 69 9 male 0 1 0 1 3 1_0-st-v2-01- 1_963.CEL AA734-HuEx- 3 1893 0 31 20 female 0 0 0 1 15 1_0-st-v2-01- 1_968.CEL AA841-HuEx- 1 1894 0 66 8 male 0 0 0 0 71 1_0-st-v2-01- 1_983.CEL AA735-HuEx- 3 1896 0 70 10 male 0 0 NA 0 102 1_0-st-v2-01- 1_887-A.CEL AA790-HuEx- 5 1648 0 67 18 female 0 0 1 1 15 1_0-st-v2-01- 1_122.CEL AA737-HuEx- 4 1656 0 66 19 female 1 0 1 1 32 1_0-st-v2-01- 1_155.CEL AA738-HuEx- 4 1660 1 66 17 male 0 0 0 0 168 1_0-st-v2-01- 1_163.CEL AA740-HuEx- 4 1664 1 68 9 male 0 1 1 1 6 1_0-st-v2-01- 1_168.CEL AA741-HuEx- 4 1666 0 78 11 male 0 0 0 1 102 1_0-st-v2-01- 1_182.CEL AA742-HuEx- 4 1677 0 68 9 female 0 1 1 1 19 1_0-st-v2-01- 1_219.CEL AA743-HuEx- 4 1685 0 61 17 male 0 0 1 0 175 1_0-st-v2-01- 1_238.CEL AA744-HuEx- 4 1686 1 55 12 male 0 1 1 1 14 1_0-st-v2-01- 1_240.CEL AA745-HuEx- 4 1687 0 74 11 male 0 1 1 1 81 1_0-st-v2-01- 1_252.CEL AA792-HuEx- 5 1692 1 71 10 male 0 0 0 1 24 1_0-st-v2-01- 1_276.CEL AA857-HuEx- 5 1693 1 80 11 male 0 1 0 1 25 1_0-st-v2-01- 1_280.CEL AA747-HuEx- 4 1703 1 71 19 male 1 0 0 1 57 1_0-st-v2-01- 1_306.CEL AA748-HuEx- 4 1707 1 68 10 male 0 0 0 0 94 1_0-st-v2-01- 1_318.CEL AA794-HuEx- 5 1708 0 65 14 male 0 0 0 0 131 1_0-st-v2-01- 1_337.CEL AA749-HuEx- 4 1710 1 68 12 female 0 0 1 1 10 1_0-st-v2-01- 1_341.CEL AA751-HuEx- 4 1713 1 80 16 male 1 0 0 1 11 1_0-st-v2-01- 1_352.CEL AA752-HuEx- 4 1714 1 74 12 male 0 0 0 1 18 1_0-st-v2-01- 1_354.CEL AA795-HuEx- 5 1720 0 71 11 male 0 1 1 1 28 1_0-st-v2-01- 1_377.CEL AA754-HuEx- 4 1722 1 63 10 male 0 0 1 1 101 1_0-st-v2-01- 1_387.CEL AA756-HuEx- 4 1726 0 53 9 male 0 0 1 1 13 1_0-st-v2-01- 1_397.CEL AA757-HuEx- 4 1728 0 81 8 male 0 0 1 1 36 1_0-st-v2-01- 1_403.CEL AA796-HuEx- 5 1729 1 55 12 male 0 0 0 0 107 1_0-st-v2-01- 1_411.CEL AA797-HuEx- 5 1730 0 75 10 female 0 0 0 0 94 1_0-st-v2-01- 1_412.CEL AA758-HuEx- 4 1732 0 60 12 male 0 0 0 0 112 1_0-st-v2-01- 1_419.CEL AA799-HuEx- 5 1734 0 67 11 male 0 1 1 1 5 1_0-st-v2-01- 1_423.CEL AA800-HuEx- 5 1736 1 69 17 female 0 0 1 0 157 1_0-st-v2-01- 1_431.CEL AA759-HuEx- 4 1741 1 70 12 male 0 0 0 0 100 1_0-st-v2-01- 1_445.CEL AA801-HuEx- 5 1744 0 79 9 male 1 1 NA 1 43 1_0-st-v2-01- 1_458.CEL AA760-HuEx- 4 1745 1 60 18 male 0 0 1 1 26 1_0-st-v2-01- 1_459.CEL AA761-HuEx- 4 1746 0 68 13 male 1 1 NA 0 111 1_0-st-v2-01- 1_467.CEL AA764-HuEx- 4 1757 0 76 13 male 1 0 1 1 40 1_0-st-v2-01- 1_508.CEL AA803-HuEx- 5 1761 NA 81 9 male 0 0 1 1 9 1_0-st-v2-01- 1_522.CEL AA765-HuEx- 4 1766 1 58 15 male 0 1 1 1 20 1_0-st-v2-01- 1_557.CEL AA804-HuEx- 5 1767 1 74 15 male 1 1 1 1 34 1_0-st-v2-01- 1_558.CEL AA768-HuEx- 4 1782 0 71 20 male 0 0 0 1 10 1_0-st-v2-01- 1_618.CEL AA769-HuEx- 4 1784 1 67 19 male 0 1 0 1 27 1_0-st-v2-01- 1_622.CEL AA807-HuEx- 5 1790 0 67 14 male 1 0 1 1 21 1_0-st-v2-01- 1_641.CEL AA770-HuEx- 4 1797 1 70 18 male 0 1 1 1 60 1_0-st-v2-01- 1_649.CEL AA773-HuEx- 4 1803 1 70 9 male 0 1 1 1 16 1_0-st-v2-01- 1_665.CEL AA775-HuEx- 4 1807 1 74 9 female 0 1 1 1 27 1_0-st-v2-01- 1_676.CEL AA809-HuEx- 5 1808 0 73 14 female 0 0 0 1 100 1_0-st-v2-01- 1_685.CEL AA810-HuEx- 5 1810 0 76 9 male 0 0 1 1 4 1_0-st-v2-01- 1_690.CEL AA852-HuEx- 4 1813 0 72 12 female 0 0 NA 0 119 1_0-st-v2-01- 1_695.CEL AA811-HuEx- 5 1815 0 46 16 male 1 0 1 1 39 1_0-st-v2-01- 1_707.CEL AA777-HuEx- 4 1818 0 62 11 male 0 0 0 1 37 1_0-st-v2-01- 1_713.CEL AA778-HuEx- 4 1819 0 59 9 male 1 1 1 1 51 1_0-st-v2-01- 1_716.CEL celfile P-Stage Race Rec_Event Rec_Event_Time qc.10.20.pass qc.15.20.pass qc.20.25.pass AA682-HuEx- 11 non- 1 9 0 0 0 1_0-st-v2-01- white 1_118.CEL AA629-HuEx- 12 non- 0 113 0 0 0 1_0-st-v2-01- white 1_132.CEL AA684-HuEx- 13 white 0 19 1 0 0 1_0-st-v2-01- 1_142.CEL AA736-HuEx- 12 white 0 179 0 0 0 1_0-st-v2-02- 2_145.CEL AA685-HuEx- 14 white 1 9 0 0 0 1_0-st-v2-01- 1_157.CEL AA739-HuEx- 11 white 0 2 1 0 0 1_0-st-v2-02- 2_166.CEL AA579-HuEx- 13 white 1 3 0 0 0 1_0-st-v2-01- 1_220.CEL AA636-HuEx- 12 white 0 12 0 0 0 1_0-st-v2-01- 1_226.CEL AA856-HuEx- 11 white 0 90 0 0 0 1_0-st-v2-01- 1_274.CEL AA746-HuEx- 14 white 1 3 0 0 0 1_0-st-v2-01- 1_292.CEL AA694-HuEx- 13 white 1 63 0 0 0 1_0-st-v2-01- 1_293.CEL AA585-HuEx- 13 white 0 3 0 0 0 1_0-st-v2-01- 1_294.CEL AA696-HuEx- 13 white 1 7 0 0 0 1_0-st-v2-01- 1_299.CEL AA697-HuEx- 12 white 1 12 1 0 0 1_0-st-v2-01- 1_311.CEL AA750-HuEx- 12 white 0 83 1 0 0 1_0-st-v2-01- 1_343.CEL AA643-HuEx- 13 white 1 14 1 1 0 1_0-st-v2-01- 1_369.CEL AA699-HuEx- 12 white 0 90 0 0 0 1_0-st-v2-01- 1_373.CEL AA753-HuEx- 12 white 0 83 0 0 0 1_0-st-v2-01- 1_376.CEL AA755-HuEx- 12 white 0 14 0 0 0 1_0-st-v2-01- 1_390.CEL AA798-HuEx- 12 white 1 6 0 0 0 1_0-st-v2-01- 1_414.CEL AA702-HuEx- 12 white 0 42 1 1 1 1_0-st-v2-01- 1_420.CEL AA704-HuEx- 13 white 1 8 0 0 0 1_0-st-v2-01- 1_444.CEL AA802-HuEx- 12 white 0 130 0 0 0 1_0-st-v2-01- 1_469.CEL AA762-HuEx- 12 white 1 8 1 0 0 1_0-st-v2-01- 1_481.CEL AA763-HuEx- 12 white 0 69 0 0 0 1_0-st-v2-01- 1_485.CEL AA594-HuEx- 12 white 0 155 0 0 0 1_0-st-v2-01- 1_493.CEL AA705-HuEx- 12 non- 0 73 1 1 1 1_0-st-v2-01- white 1_506.CEL AA597-HuEx- 13 white 1 8 1 1 1 1_0-st-v2-01- 1_529_2.CEL AA805-HuEx- 12 white 1 13 0 0 0 1_0-st-v2-01- 1_560.CEL AA766-HuEx- 11 white 0 203 1 0 0 1_0-st-v2-01- 1_562.CEL AA767-HuEx- 13 white 1 9 0 0 0 1_0-st-v2-01- 1_569.CEL AA806-HuEx- 14 white 0 8 0 0 0 1_0-st-v2-01- 1_594.CEL AA602-HuEx- 12 white 0 169 0 0 0 1_0-st-v2-01- 1_623.CEL AA771-HuEx- 13 white 0 124 1 0 0 1_0-st-v2-01- 1_651.CEL AA772-HuEx- 12 white 0 76 1 0 0 1_0-st-v2-01- 1_652.CEL AA808-HuEx- 13 non- 0 6 0 0 0 1_0-st-v2-01- white 1_656.CEL AA849-HuEx- 13 white 1 12 1 1 1 1_0-st-v2-01- 1_664.CEL AA774-HuEx- 13 white 0 78 0 0 0 1_0-st-v2-01- 1_666.CEL AA662-HuEx- 12 white 0 143 1 0 0 1_0-st-v2-01- 1_703.CEL AA607-HuEx- 14 white 0 6 0 0 0 1_0-st-v2-01- 1_709.CEL AA719-HuEx- 13 non- 0 69 1 0 0 1_0-st-v2-01- white 1_726.CEL AA721-HuEx- 13 non- 0 20 0 0 0 1_0-st-v2-01- white 1_756.CEL AA666-HuEx- 13 white 1 20 1 1 0 1_0-st-v2-01- 1_763.CEL AA722-HuEx- 12 white 0 159 0 0 0 1_0-st-v2-01- 1_777.CEL AA780-HuEx- 13 white 0 76 0 0 0 1_0-st-v2-01- 1_779.CEL AA781-HuEx- 12 white 0 18 1 1 0 1_0-st-v2-02- 2_800.CEL AA667-HuEx- 12 white 0 112 1 1 1 1_0-st-v2-01- 1_826.CEL AA619-HuEx- 12 white 0 60 0 0 0 1_0-st-v2-01- 1_881.CEL AA625-HuEx- 13 white 0 120 0 0 0 1_0-st-v2-01- 1_956.CEL AA732-HuEx- 14 white 1 13 1 1 1 1_0-st-v2-01- 1_957.CEL AA680-HuEx- 12 white 0 172 0 0 0 1_0-st-v2-01- 1_958.CEL AA733-HuEx- 13 white 1 7 1 1 0 1_0-st-v2-01- 1_959.CEL AA574-HuEx- 14 white 1 56 1 1 0 1_0-st-v2-01- 1_120.CEL AA628-HuEx- 13 white 0 27 1 1 0 1_0-st-v2-01- 1_130.CEL AA683-HuEx- 14 white 0 11 1 1 1 1_0-st-v2-01- 1_135.CEL AA575-HuEx- 13 white 0 3 1 1 1 1_0-st-v2-01- 1_143.CEL AA630-HuEx- 13 non- 0 73 1 1 0 1_0-st-v2-01- white 1_144.CEL AA846-HuEx- 13 non- 0 68 1 1 1 1_0-st-v2-01- white 1_159.CEL AA576-HuEx- 14 white 0 71 1 1 1 1_0-st-v2-01- 1_162.CEL AA686-HuEx- 12 white 0 149 1 1 1 1_0-st-v2-01- 1_165.CEL AA687-HuEx- 12 non- 0 133 1 1 1 1_0-st-v2-01- white 1_167.CEL AA631-HuEx- 11 white 1 14 1 1 0 1_0-st-v2-01- 1_173.CEL AA577-HuEx- 13 white 1 14 1 1 1 1_0-st-v2-01- 1_184.CEL AA578-HuEx- 13 white 1 4 1 1 1 1_0-st-v2-01- 1_186.CEL AA632-HuEx- 14 white 1 24 1 1 1 1_0-st-v2-01- 1_195.CEL AA848-HuEx- 14 white 0 107 1 1 1 1_0-st-v2-01- 1_198.CEL AA689-HuEx- 13 white 1 4 1 1 1 1_0-st-v2-01- 1_199.CEL AA633-HuEx- 13 white 0 31 1 1 1 1_0-st-v2-01- 1_203.CEL AA690-HuEx- 13 white 0 108 1 1 0 1_0-st-v2-01- 1_211.CEL AA634-HuEx- 14 white 1 7 1 1 1 1_0-st-v2-01- 1_213.CEL AA691-HuEx- 12 white 0 25 1 1 1 1_0-st-v2-01- 1_214.CEL AA635-HuEx- 13 white 0 78 1 1 1 1_0-st-v2-01- 1_218.CEL AA692-HuEx- 13 white 0 5 1 1 1 1_0-st-v2-01- 1_224.CEL AA580-HuEx- 13 white 0 45 1 1 0 1_0-st-v2-01- 1_227.CEL AA637-HuEx- 13 white 1 7 1 1 1 1_0-st-v2-01- 1_228.CEL AA693-HuEx- 12 white 1 18 1 1 1 1_0-st-v2-01- 1_230.CEL AA581-HuEx- 14 white 0 90 1 1 1 1_0-st-v2-01- 1_235.CEL AA638-HuEx- 13 white 1 37 1 1 1 1_0-st-v2-01- 1_258.CEL AA639-HuEx- 13 white 0 10 1 1 1 1_0-st-v2-01- 1_267.CEL AA582-HuEx- 12 white 1 8 1 1 1 1_0-st-v2-01- 1_272.CEL AA640-HuEx- 13 white 1 12 1 1 1 1_0-st-v2-01- 1_281.CEL AA583-HuEx- 13 white 1 23 1 1 0 1_0-st-v2-01- 1_284.CEL AA584-HuEx- 14 white 1 7 1 1 0 1_0-st-v2-01- 1_286.CEL AA695-HuEx- 13 white 0 72 1 1 0 1_0-st-v2-01- 1_295.CEL AA586-HuEx- 13 white 1 20 1 1 1 1_0-st-v2-01- 1_296.CEL AA587-HuEx- 12 white 0 175 1 1 0 1_0-st-v2-01- 1_309.CEL AA641-HuEx- 12 white 1 50 1 1 1 1_0-st-v2-01- 1_314.CEL AA847-HuEx- 12 white 1 24 1 1 1 1_0-st-v2-01- 1_338.CEL AA698-HuEx- 13 white 0 94 1 1 1 1_0-st-v2-01- 1_342.CEL AA642-HuEx- 12 white 1 12 1 1 1 1_0-st-v2-01- 1_368.CEL AA588-HuEx- 11 white 0 112 1 1 1 1_0-st-v2-01- 1_375.CEL AA644-HuEx- 13 white 1 35 1 1 1 1_0-st-v2-01- 1_382.CEL AA700-HuEx- 13 white 1 18 1 1 1 1_0-st-v2-01- 1_393.CEL AA589-HuEx- 14 white 1 70 1 1 1 1_0-st-v2-01- 1_396.CEL AA701-HuEx- 14 white 0 151 1 1 1 1_0-st-v2-01- 1_402.CEL AA590-HuEx- 13 white 1 3 1 1 1 1_0-st-v2-01- 1_430.CEL AA591-HuEx- 12 white 0 111 1 1 1 1_0-st-v2-01- 1_436.CEL AA645-HuEx- 13 white 0 105 1 1 1 1_0-st-v2-01- 1_437.CEL AA703-HuEx- 14 white 1 40 1 1 1 1_0-st-v2-01- 1_441.CEL AA646-HuEx- 14 white 0 153 1 1 1 1_0-st-v2-01- 1_454.CEL AA592-HuEx- 12 white 0 170 1 1 1 1_0-st-v2-01- 1_455.CEL AA593-HuEx- 12 non- 0 112 1 1 1 1_0-st-v2-01- white 1_475.CEL AA647-HuEx- 13 white 0 119 1 1 1 1_0-st-v2-01- 1_476.CEL AA648-HuEx- 13 white 0 132 1 1 1 1_0-st-v2-01- 1_477.CEL AA649-HuEx- 13 white 1 4 1 1 1 1_0-st-v2-01- 1_479.CEL AA650-HuEx- 12 white 1 48 1 1 1 1_0-st-v2-01- 1_504.CEL AA651-HuEx- 13 white 0 17 1 1 1 1_0-st-v2-01- 1_510.CEL AA595-HuEx- 13 non- 0 97 1 1 1 1_0-st-v2-01- white 1_512.CEL AA706-HuEx- 13 white 1 4 1 1 1 1_0-st-v2-01- 1_517.CEL AA596-HuEx- 12 white 1 4 1 1 0 1_0-st-v2-01- 1_528.CEL AA845-HuEx- 12 non- 0 69 1 1 1 1_0-st-v2-01- white 1_547.CEL AA598-HuEx- 14 white 1 3 1 1 1 1_0-st-v2-01- 1_552.CEL AA707-HuEx- 13 white 0 115 1 1 1 1_0-st-v2-01- 1_567.CEL AA599-HuEx- 11 white 0 19 1 1 1 1_0-st-v2-01- 1_579.CEL AA600-HuEx- 13 white 0 66 1 1 1 1_0-st-v2-01- 1_586.CEL AA653-HuEx- 12 white 0 36 1 1 1 1_0-st-v2-01- 1_591.CEL AA654-HuEx- 14 white 1 23 1 1 1 1_0-st-v2-01- 1_596.CEL AA655-HuEx- 12 white 0 128 1 1 1 1_0-st-v2-01- 1_597.CEL AA656-HuEx- 13 white 1 18 1 1 1 1_0-st-v2-01- 1_600.CEL AA657-HuEx- 14 white 1 65 1 1 1 1_0-st-v2-01- 1_608.CEL AA601-HuEx- 13 white 0 13 1 1 1 1_0-st-v2-01- 1_612.CEL AA708-HuEx- 14 white 0 2 1 1 1 1_0-st-v2-01- 1_616.CEL AA709-HuEx- 13 white 0 8 1 1 1 1_0-st-v2-01- 1_619.CEL AA603-HuEx- 14 white 0 127 1 1 0 1_0-st-v2-01- 1_626.CEL AA658-HuEx- 13 white 0 91 1 1 1 1_0-st-v2-01- 1_627.CEL AA659-HuEx- 12 white 0 11 1 1 1 1_0-st-v2-01- 1_630_2.CEL AA604-HuEx- 12 non- 1 15 1 1 1 1_0-st-v2-01- white 1_640.CEL AA710-HuEx- 12 white 0 107 1 1 1 1_0-st-v2-01- 1_643.CEL AA660-HuEx- 13 white 1 8 1 1 1 1_0-st-v2-01- 1_644.CEL AA711-HuEx- 14 white 1 4 1 1 1 1_0-st-v2-01- 1_645.CEL AA712-HuEx- 13 white 0 108 1 1 1 1_0-st-v2-01- 1_646.CEL AA713-HuEx- 12 non- 1 16 1 1 1 1_0-st-v2-01- white 1_647.CEL AA605-HuEx- 14 white 0 69 1 1 1 1_0-st-v2-01- 1_648.CEL AA714-HuEx- 11 white 1 15 1 1 1 1_0-st-v2-01- 1_655.CEL AA716-HuEx- 13 white 0 168 1 1 0 1_0-st-v2-01- 1_668.CEL AA661-HuEx- 13 white 0 172 1 1 1 1_0-st-v2-01- 1_673.CEL AA606-HuEx- 13 white 0 118 1 1 1 1_0-st-v2-01- 1_686.CEL AA717-HuEx- 13 non- 1 8 1 1 1 1_0-st-v2-01- white 1_691.CEL AA718-HuEx- 12 white 0 135 1 1 1 1_0-st-v2-01- 1_693.CEL AA663-HuEx- 12 white 0 7 1 1 1 1_0-st-v2-01- 1_708.CEL AA608-HuEx- 12 white 1 76 1 1 1 1_0-st-v2-01- 1_717.CEL AA609-HuEx- 13 white 1 34 1 1 1 1_0-st-v2-01- 1_722.CEL AA610-HuEx- 13 white 0 31 1 1 1 1_0-st-v2-01- 1_734.CEL AA664-HuEx- 14 white 1 63 1 1 1 1_0-st-v2-01- 1_738.CEL AA611-HuEx- 14 white 1 12 1 1 1 1_0-st-v2-01- 1_740.CEL AA665-HuEx- 13 non- 0 50 1 1 1 1_0-st-v2-01- white 1_744.CEL AA612-HuEx- 13 white 1 12 1 1 1 1_0-st-v2-01- 1_750.CEL AA720-HuEx- 12 white 1 4 1 1 1 1_0-st-v2-01- 1_752.CEL AA613-HuEx- 12 white 0 10 1 1 1 1_0-st-v2-01- 1_753.CEL AA614-HuEx- 12 white 0 129 1 1 1 1_0-st-v2-01- 1_767.CEL AA615-HuEx- 12 white 1 11 1 1 1 1_0-st-v2-01- 1_781.CEL AA723-HuEx- 12 white 1 7 1 1 1 1_0-st-v2-01- 1_816.CEL AA724-HuEx- 14 white 1 44 1 1 1 1_0-st-v2-01- 1_822.CEL AA668-HuEx- 14 non- 1 8 1 1 1 1_0-st-v2-01- white 1_827.CEL AA669-HuEx- 12 white 0 160 1 1 1 1_0-st-v2-01- 1_828.CEL AA670-HuEx- 12 white 1 14 1 1 1 1_0-st-v2-01- 1_832.CEL AA616-HuEx- 12 white 0 90 1 1 1 1_0-st-v2-01- 1_842.CEL AA671-HuEx- 13 non- 1 11 1 1 1 1_0-st-v2-01- white 1_844.CEL AA725-HuEx- 12 white 0 110 1 1 1 1_0-st-v2-01- 1_846.CEL AA672-HuEx- 14 white 0 19 1 1 1 1_0-st-v2-01- 1_850.CEL AA617-HuEx- 12 white 0 114 1 1 0 1_0-st-v2-01- 1_852.CEL AA673-HuEx- 13 white 0 3 1 1 1 1_0-st-v2-01- 1_857.CEL AA618-HuEx- 14 white 1 3 1 1 1 1_0-st-v2-01- 1_869.CEL AA726-HuEx- 13 white 0 92 1 1 1 1_0-st-v2-01- 1_872.CEL AA674-HuEx- 12 white 0 175 1 1 1 1_0-st-v2-01- 1_877.CEL AA675-HuEx- 14 white 0 174 1 1 1 1_0-st-v2-01- 1_878.CEL AA727-HuEx- 13 white 1 16 1 1 1 1_0-st-v2-01- 1_892.CEL AA620-HuEx- 14 white 1 11 1 1 1 1_0-st-v2-01- 1_894.CEL AA728-HuEx- 12 white 1 33 1 1 0 1_0-st-v2-01- 1_895.CEL AA621-HuEx- 13 non- 1 9 1 1 1 1_0-st-v2-01- white 1_902.CEL AA676-HuEx- 12 white 0 47 1 1 1 1_0-st-v2-01- 1_906.CEL AA622-HuEx- 13 white 0 130 1 1 0 1_0-st-v2-01- 1_907.CEL AA677-HuEx- 14 white 1 5 1 1 1 1_0-st-v2-01- 1_911.CEL AA678-HuEx- 13 white 1 12 1 1 1 1_0-st-v2-01- 1_914.CEL AA729-HuEx- 13 non- 0 8 1 1 1 1_0-st-v2-01- white 1_916.CEL AA623-HuEx- 13 white 1 5 1 1 1 1_0-st-v2-01- 1_924.CEL AA730-HuEx- 12 white 0 61 1 1 0 1_0-st-v2-01- 1_926.CEL AA731-HuEx- 14 white 1 10 1 1 1 1_0-st-v2-01- 1_928.CEL AA679-HuEx- 14 non- 1 32 1 1 1 1_0-st-v2-01- white 1_932.CEL AA624-HuEx- 13 white 0 76 1 1 1 1_0-st-v2-01- 1_951.CEL AA681-HuEx- 11 white 0 23 1 1 1 1_0-st-v2-01- 1_961.CEL AA626-HuEx- 14 white 0 3 1 1 1 1_0-st-v2-01- 1_963.CEL AA734-HuEx- 13 white 1 3 1 1 1 1_0-st-v2-01- 1_968.CEL AA841-HuEx- 12 non- 0 71 1 1 1 1_0-st-v2-01- white 1_983.CEL AA735-HuEx- 12 white 0 102 1 1 1 1_0-st-v2-01- 1_887-A.CEL AA790-HuEx- 13 white 0 15 1 1 0 1_0-st-v2-01- 1_122.CEL AA737-HuEx- 13 white 1 9 1 1 0 1_0-st-v2-01- 1_155.CEL AA738-HuEx- 12 white 0 168 1 1 1 1_0-st-v2-01- 1_163.CEL AA740-HuEx- 13 white 0 6 1 1 0 1_0-st-v2-01- 1_168.CEL AA741-HuEx- 13 white 0 102 1 1 1 1_0-st-v2-01- 1_182.CEL AA742-HuEx- 13 white 1 6 1 1 0 1_0-st-v2-01- 1_219.CEL AA743-HuEx- 13 white 0 175 1 1 1 1_0-st-v2-01- 1_238.CEL AA744-HuEx- 14 non- 1 12 1 1 0 1_0-st-v2-01- white 1_240.CEL AA745-HuEx- 13 white 0 81 1 1 1 1_0-st-v2-01- 1_252.CEL AA792-HuEx- 13 non- 1 15 1 1 0 1_0-st-v2-01- white 1_276.CEL AA857-HuEx- 13 white 1 25 1 1 1 1_0-st-v2-01- 1_280.CEL AA747-HuEx- 13 white 1 57 1 1 1 1_0-st-v2-01- 1_306.CEL AA748-HuEx- 13 white 0 94 1 1 1 1_0-st-v2-01- 1_318.CEL AA794-HuEx- 12 white 0 131 1 1 0 1_0-st-v2-01- 1_337.CEL AA749-HuEx- 13 white 1 6 1 1 1 1_0-st-v2-01- 1_341.CEL AA751-HuEx- 13 white 1 7 1 1 0 1_0-st-v2-01- 1_352.CEL AA752-HuEx- 13 white 1 12 1 1 1 1_0-st-v2-01- 1_354.CEL AA795-HuEx- 14 white 1 20 1 1 1 1_0-st-v2-01- 1_377.CEL AA754-HuEx- 12 non- 1 90 1 1 0 1_0-st-v2-01- white 1_387.CEL AA756-HuEx- 13 white 0 13 1 1 1 1_0-st-v2-01- 1_397.CEL AA757-HuEx- 13 white 0 36 1 1 0 1_0-st-v2-01- 1_403.CEL AA796-HuEx- 13 white 0 107 1 1 1 1_0-st-v2-01- 1_411.CEL AA797-HuEx- 13 white 0 94 1 1 1 1_0-st-v2-01- 1_412.CEL AA758-HuEx- 12 white 0 112 1 1 1 1_0-st-v2-01- 1_419.CEL AA799-HuEx- 14 white 1 5 1 1 1 1_0-st-v2-01- 1_423.CEL AA800-HuEx- 13 white 0 157 1 1 1 1_0-st-v2-01- 1_431.CEL AA759-HuEx- 14 non- 0 100 1 1 1 1_0-st-v2-01- white 1_445.CEL AA801-HuEx- 14 non- 1 12 1 1 1 1_0-st-v2-01- white 1_458.CEL AA760-HuEx- 13 white 0 26 1 1 1 1_0-st-v2-01- 1_459.CEL AA761-HuEx- 12 white 0 111 1 1 1 1_0-st-v2-01- 1_467.CEL AA764-HuEx- 13 white 1 26 1 1 0 1_0-st-v2-01- 1_508.CEL AA803-HuEx- 14 white 0 9 1 1 1 1_0-st-v2-01- 1_522.CEL AA765-HuEx- 14 white 1 16 1 1 0 1_0-st-v2-01- 1_557.CEL AA804-HuEx- 14 white 1 28 1 1 1 1_0-st-v2-01- 1_558.CEL AA768-HuEx- 12 white 1 4 1 1 0 1_0-st-v2-01- 1_618.CEL AA769-HuEx- 11 white 1 23 1 1 1 1_0-st-v2-01- 1_622.CEL AA807-HuEx- 13 white 1 11 1 1 1 1_0-st-v2-01- 1_641.CEL AA770-HuEx- 13 white 0 60 1 1 1 1_0-st-v2-01- 1_649.CEL AA773-HuEx- 13 white 1 7 1 1 1 1_0-st-v2-01- 1_665.CEL AA775-HuEx- 13 white 1 15 1 1 1 1_0-st-v2-01- 1_676.CEL AA809-HuEx- 13 white 0 100 1 1 0 1_0-st-v2-01- 1_685.CEL AA810-HuEx- 14 white 1 4 1 1 1 1_0-st-v2-01- 1_690.CEL AA852-HuEx- 12 non- 0 119 1 1 0 1_0-st-v2-01- white 1_695.CEL AA811-HuEx- 12 white 0 39 1 1 1 1_0-st-v2-01- 1_707.CEL AA777-HuEx- 12 non- 1 14 1 1 0 1_0-st-v2-01- white 1_713.CEL AA778-HuEx- T4 white 1 8 1 1 0 1_0-st-v2-01- 1_716.CEL Percent celfile qc.20.30.pass qc.30.40.pass Present Set GC GCC AA682-HuEx- 0 0 9.65862 NA NA NA 1_0-st-v2-01- 1_118.CEL AA629-HuEx- 0 0 16.4473 NA NA NA 1_0-st-v2-01- 1_132.CEL AA684-HuEx- 0 0 10.8961 NA NA NA 1_0-st-v2-01- 1_142.CEL AA736-HuEx- 0 0 7.92935 NA NA NA 1_0-st-v2-02- 2_145.CEL AA685-HuEx- 0 0 8.87618 NA NA NA 1_0-st-v2-01- 1_157.CEL AA739-HuEx- 0 0 12.3508 NA NA NA 1_0-st-v2-02- 2_166.CEL AA579-HuEx- 0 0 42.4653 NA NA NA 1_0-st-v2-01- 1_220.CEL AA636-HuEx- 0 0 9.35561 NA NA NA 1_0-st-v2-01- 1_226.CEL AA856-HuEx- 0 0 27.2338 NA NA NA 1_0-st-v2-01- 1_274.CEL AA746-HuEx- 0 0 9.0163 NA NA NA 1_0-st-v2-01- 1_292.CEL AA694-HuEx- 0 0 13.7011 NA NA NA 1_0-st-v2-01- 1_293.CEL AA585-HuEx- 0 0 50.6811 NA NA NA 1_0-st-v2-01- 1_294.CEL AA696-HuEx- 0 0 39.3282 NA NA NA 1_0-st-v2-01- 1_299.CEL AA697-HuEx- 0 0 13.9019 NA NA NA 1_0-st-v2-01- 1_311.CEL AA750-HuEx- 0 0 14.9777 NA NA NA 1_0-st-v2-01- 1_343.CEL AA643-HuEx- 0 0 16.8539 NA NA NA 1_0-st-v2-01- 1_369.CEL AA699-HuEx- 0 0 10.7207 NA NA NA 1_0-st-v2-01- 1_373.CEL AA753-HuEx- 0 0 6.79608 NA NA NA 1_0-st-v2-01- 1_376.CEL AA755-HuEx- 0 0 16.6886 NA NA NA 1_0-st-v2-01- 1_390.CEL AA798-HuEx- 0 0 12.711 NA NA NA 1_0-st-v2-01- 1_414.CEL AA702-HuEx- 1 0 27.9437 NA NA NA 1_0-st-v2-01- 1_420.CEL AA704-HuEx- 0 0 6.87799 NA NA NA 1_0-st-v2-01- 1_444.CEL AA802-HuEx- 0 0 18.9741 NA NA NA 1_0-st-v2-01- 1_469.CEL AA762-HuEx- 0 0 13.4185 NA NA NA 1_0-st-v2-01- 1_481.CEL AA763-HuEx- 0 0 36.6512 NA NA NA 1_0-st-v2-01- 1_485.CEL AA594-HuEx- 0 0 11.2307 NA NA NA 1_0-st-v2-01- 1_493.CEL AA705-HuEx- 1 0 29.4208 NA NA NA 1_0-st-v2-01- 1_506.CEL AA597-HuEx- 1 0 20.2292 NA NA NA 1_0-st-v2-01- 1_529_2.CEL AA805-HuEx- 0 0 19.9348 NA NA NA 1_0-st-v2-01- 1_560.CEL AA766-HuEx- 0 0 10.8515 NA NA NA 1_0-st-v2-01- 1_562.CEL AA767-HuEx- 0 0 44.9042 NA NA NA 1_0-st-v2-01- 1_569.CEL AA806-HuEx- 0 0 35.3453 NA NA NA 1_0-st-v2-01- 1_594.CEL AA602-HuEx- 0 0 5.91103 NA NA NA 1_0-st-v2-01- 1_623.CEL AA771-HuEx- 0 0 14.8189 NA NA NA 1_0-st-v2-01- 1_651.CEL AA772-HuEx- 0 0 14.828 NA NA NA 1_0-st-v2-01- 1_652.CEL AA808-HuEx- 0 0 16.3954 NA NA NA 1_0-st-v2-01- 1_656.CEL AA849-HuEx- 1 0 20.492 NA NA NA 1_0-st-v2-01- 1_664.CEL AA774-HuEx- 0 0 42.3303 NA NA NA 1_0-st-v2-01- 1_666.CEL AA662-HuEx- 0 0 10.1421 NA NA NA 1_0-st-v2-01- 1_703.CEL AA607-HuEx- 0 0 19.8998 NA NA NA 1_0-st-v2-01- 1_709.CEL AA719-HuEx- 0 0 14.0108 NA NA NA 1_0-st-v2-01- 1_726.CEL AA721-HuEx- 0 0 19.172 NA NA NA 1_0-st-v2-01- 1_756.CEL AA666-HuEx- 0 0 16.328 NA NA NA 1_0-st-v2-01- 1_763.CEL AA722-HuEx- 0 0 7.71556 NA NA NA 1_0-st-v2-01- 1_777.CEL AA780-HuEx- 0 0 34.3543 NA NA NA 1_0-st-v2-01- 1_779.CEL AA781-HuEx- 0 0 15.2955 NA NA NA 1_0-st-v2-02- 2_800.CEL AA667-HuEx- 1 0 23.8016 NA NA NA 1_0-st-v2-01- 1_826.CEL AA619-HuEx- 0 0 38.3754 NA NA NA 1_0-st-v2-01- 1_881.CEL AA625-HuEx- 0 0 15.5688 NA NA NA 1_0-st-v2-01- 1_956.CEL AA732-HuEx- 1 0 25.7658 NA NA NA 1_0-st-v2-01- 1_957.CEL AA680-HuEx- 0 0 5.83528 NA NA NA 1_0-st-v2-01- 1_958.CEL AA733-HuEx- 0 0 19.708 NA NA NA 1_0-st-v2-01- 1_959.CEL AA574-HuEx- 0 0 18.3853 trn 0.714286 0.498007 1_0-st-v2-01- 1_120.CEL AA628-HuEx- 0 0 17.1642 trn 0.571429 0.566006 1_0-st-v2-01- 1_130.CEL AA683-HuEx- 0 0 28.4031 trn 0.333333 0.12917 1_0-st-v2-01- 1_135.CEL AA575-HuEx- 1 1 33.2624 trn 0.428571 NA 1_0-st-v2-01- 1_143.CEL AA630-HuEx- 0 0 19.9707 trn 0.619048 0.470843 1_0-st-v2-01- 1_144.CEL AA846-HuEx- 1 1 32.0879 trn 0.761905 0.90905 1_0-st-v2-01- 1_159.CEL AA576-HuEx- 1 0 23.9048 trn 0.285714 0.111354 1_0-st-v2-01- 1_162.CEL AA686-HuEx- 1 0 26.9212 trn 0.285714 0.123361 1_0-st-v2-01- 1_165.CEL AA687-HuEx- 1 1 82.5709 trn 0.238095 0.100865 1_0-st-v2-01- 1_167.CEL AA631-HuEx- 0 0 19.8189 trn 0.285714 0.395288 1_0-st-v2-01- 1_173.CEL AA577-HuEx- 1 0 25.9857 trn 0.571429 0.594805 1_0-st-v2-01- 1_184.CEL AA578-HuEx- 1 1 40.6266 trn 0.666667 0.902539 1_0-st-v2-01- 1_186.CEL AA632-HuEx- 1 1 86.5852 trn 0.428571 0.505144 1_0-st-v2-01- 1_195.CEL AA848-HuEx- 1 1 80.4682 trn 0.190476 0.303659 1_0-st-v2-01- 1_198.CEL AA689-HuEx- 1 0 25.311 trn 0.285714 0.204983 1_0-st-v2-01- 1_199.CEL AA633-HuEx- 1 0 26.1989 trn 0.285714 0.132257 1_0-st-v2-01- 1_203.CEL AA690-HuEx- 0 0 15.5687 trn 0.619048 0.379821 1_0-st-v2-01- 1_211.CEL AA634-HuEx- 1 0 21.8405 trn 0.714286 0.533344 1_0-st-v2-01- 1_213.CEL AA691-HuEx- 1 1 81.409 trn 0.190476 0.062719 1_0-st-v2-01- 1_214.CEL AA635-HuEx- 1 1 43.8173 trn 0.47619 0.797366 1_0-st-v2-01- 1_218.CEL AA692-HuEx- 1 0 20.0017 trn 0.47619 0.617298 1_0-st-v2-01- 1_224.CEL AA580-HuEx- 0 0 19.7804 trn 0.285714 0.143443 1_0-st-v2-01- 1_227.CEL AA637-HuEx- 1 1 81.9929 trn 0.380952 0.248786 1_0-st-v2-01- 1_228.CEL AA693-HuEx- 1 0 26.0979 trn 0.380952 0.454064 1_0-st-v2-01- 1_230.CEL AA581-HuEx- 1 1 41.302 trn 0.238095 0.069548 1_0-st-v2-01- 1_235.CEL AA638-HuEx- 1 0 29.038 trn 0.666667 0.873885 1_0-st-v2-01- 1_258.CEL AA639-HuEx- 1 1 85.1116 trn 0.52381 0.746824 1_0-st-v2-01- 1_267.CEL AA582-HuEx- 1 0 24.8041 trn 0.428571 0.677148 1_0-st-v2-01- 1_272.CEL AA640-HuEx- 1 0 26.1611 trn 0.619048 0.814131 1_0-st-v2-01- 1_281.CEL AA583-HuEx- 0 0 17.5976 trn 0.333333 0.685014 1_0-st-v2-01- 1_284.CEL AA584-HuEx- 0 0 19.1203 trn 0.428571 0.498466 1_0-st-v2-01- 1_286.CEL AA695-HuEx- 0 0 18.3506 trn 0.619048 NA 1_0-st-v2-01- 1_295.CEL AA586-HuEx- 1 1 34.6682 trn 0.571429 NA 1_0-st-v2-01- 1_296.CEL AA587-HuEx- 0 0 19.3913 trn 0.285714 0.097794 1_0-st-v2-01- 1_309.CEL AA641-HuEx- 1 1 41.2583 trn 0.428571 0.67796 1_0-st-v2-01- 1_314.CEL AA847-HuEx- 1 1 41.036 trn 0619048 0.379821 1_0-st-v2-01- 1_338.CEL AA698-HuEx- 1 0 27.0603 trn 0.666667 0.598666 1_0-st-v2-01- 1_342.CEL AA642-HuEx- 1 1 38.6051 trn 0.666667 0.790496 1_0-st-v2-01- 1_368.CEL AA588-HuEx- 1 1 30.4825 trn 0.428571 0.419424 1_0-st-v2-01- 1_375.CEL AA644-HuEx- 1 1 36.1872 trn 0.619048 0346308 1_0-st-v2-01- 1_382.CEL AA700-HuEx- 1 1 35.3889 trn 0.47619 0.777976 1_0-st-v2-01- 1_393.CEL AA589-HuEx- 1 1 30.6512 trn 0.380952 0.377586 1_0-st-v2-01- 1_396.CEL AA701-HuEx- 1 0 26.802 trn 0.333333 0.139276 1_0-st-v2-01- 1_402.CEL AA590-HuEx- 1 0 21.0747 trn 0.714286 0.638435 1_0-st-v2-01- 1_430.CEL AA591-HuEx- 1 1 33.7772 trn 0.333333 0.238398 1_0-st-v2-01- 1_436.CEL AA645-HuEx- 1 1 39.2918 trn 0.571429 0.70012 1_0-st-v2-01- 1_437.CEL AA703-HuEx- 1 0 20.3831 trn 0.809524 NA 1_0-st-v2-01- 1_441.CEL AA646-HuEx- 1 1 36.2867 trn 0.285714 0.181807 1_0-st-v2-01- 1_454.CEL AA592-HuEx- 1 0 27.4093 trn 0.333333 NA 1_0-st-v2-01- 1_455.CEL AA593-HuEx- 1 1 31.6379 trn 0.285714 0.092794 1_0-st-v2-01- 1_475.CEL AA647-HuEx- 1 1 43.307 trn 0.47619 0.206802 1_0-st-v2-01- 1_476.CEL AA648-HuEx- 1 1 30.9764 trn 0.428571 0.237764 1_0-st-v2-01- 1_477.CEL AA649-HuEx- 1 1 42.7328 trn 0.428571 0.652129 1_0-st-v2-01- 1_479.CEL AA650-HuEx- 1 0 27.342 trn 0.285714 0.139056 1_0-st-v2-01- 1_504.CEL AA651-HuEx- 1 1 36.3301 trn 0.571429 NA 1_0-st-v2-01- 1_510.CEL AA595-HuEx- 0 0 26.5464 trn 0.52381 NA 1_0-st-v2-01- 1_512.CEL AA706-HuEx- 1 1 36.0797 trn 0.714286 0.547747 1_0-st-v2-01- 1_517.CEL AA596-HuEx- 0 0 15.7068 trn 0.47619 0.820845 1_0-st-v2-01- 1_528.CEL AA845-HuEx- 0 0 25.0048 trn 0.47619 NA 1_0-st-v2-01- 1_547.CEL AA598-HuEx- 1 1 36.0621 trn 0.47619 NA 1_0-st-v2-01- 1_552.CEL AA707-HuEx- 1 0 25.8532 trn 0.380952 0.578042 1_0-st-v2-01- 1_567.CEL AA599-HuEx- 1 1 38.0983 trn 0.619048 0.674044 1_0-st-v2-01- 1_579.CEL AA600-HuEx- 1 0 24.5212 trn 0.333333 0.386568 1_0-st-v2-01- 1_586.CEL AA653-HuEx- 1 0 37.2906 trn 0.285714 0.157341 1_0-st-v2-01- 1_591.CEL AA654-HuEx- 1 1 41.2555 trn 0.666667 0.894613 1_0-st-v2-01- 1_596.CEL AA655-HuEx- 1 1 33.7621 trn 0.285714 0.092794 1_0-st-v2-01- 1_597.CEL AA656-HuEx- 1 0 23.2758 trn 0.761905 0.582902 1_0-st-v2-01- 1_600.CEL AA657-HuEx- 1 0 27.325 trn 0.619048 0.4627 1_0-st-v2-01- 1_608.CEL AA601-HuEx- 1 0 21.2178 trn 0.428571 0.604777 1_0-st-v2-01- 1_612.CEL AA708-HuEx- 1 0 23.2791 trn 0.666667 0.826174 1_0-st-v2-01- 1_616.CEL AA709-HuEx- 1 0 28.8194 trn 0.52381 0.302628 1_0-st-v2-01- 1_619.CEL AA603-HuEx- 0 0 16.4245 trn 0.571429 0.492965 1_0-st-v2-01- 1_626.CEL AA658-HuEx- 1 1 39.2526 trn 0.52381 0.304144 1_0-st-v2-01- 1_627.CEL AA659-HuEx- 1 0 21.7147 trn 0.47619 0.389655 1_0-st-v2-01- 1_630_2.CEL AA604-HuEx- 1 0 25.4692 trn 0.666667 0.758916 1_0-st-v2-01- 1_640.CEL AA710-HuEx- 1 0 26.8504 trn 0.142857 0.24166 1_0-st-v2-01- 1_643.CEL AA660-HuEx- 1 1 33.4575 trn 0.714286 NA 1_0-st-v2-01- 1_644.CEL AA711-HuEx- 1 1 34.7123 trn 0.761905 0.931752 1_0-st-v2-01- 1_645.CEL AA712-HuEx- 1 1 81.0272 trn 0.238095 0.098265 1_0-st-v2-01- 1_646.CEL AA713-HuEx- 1 0 22.7 trn 0.285714 0.486177 1_0-st-v2-01- 1_647.CEL AA605-HuEx- 1 0 23.5929 trn 0.095238 0.037519 1_0-st-v2-01- 1_648.CEL AA714-HuEx- 1 1 87.2293 trn 0.571429 0.834829 1_0-st-v2-01- 1_655.CEL AA716-HuEx- 0 0 17.8395 trn 0.142857 0.061342 1_0-st-v2-01- 1_668.CEL AA661-HuEx- 1 1 88.4027 trn 0.47619 0.460393 1_0-st-v2-01- 1_673.CEL AA606-HuEx- 1 0 24.6641 trn 0.47619 NA 1_0-st-v2-01- 1_686.CEL AA717-HuEx- 1 1 87.6296 trn 0.619048 0.850444 1_0-st-v2-01- 1_691.CEL AA718-HuEx- 1 0 22.3175 trn 0.333333 0.627867 1_0-st-v2-01- 1_693.CEL AA663-HuEx- 1 1 85.2171 trn 0.333333 0.116673 1_0-st-v2-01- 1_708.CEL AA608-HuEx- 1 1 82.3636 trn 0.619048 0.393576 1_0-st-v2-01- 1_717.CEL AA609-HuEx- 1 1 80.2338 trn 0.809524 0.609537 1_0-st-v2-01- 1_722.CEL AA610-HuEx- 1 0 20.643 trn 0.666667 0.549068 1_0-st-v2-01- 1_734.CEL AA664-HuEx- 1 1 51.905 trn 0.333333 NA 1_0-st-v2-01- 1_738.CEL AA611-HuEx- 1 0 88.6613 trn 0.619048 0.899792 1_0-st-v2-01- 1_740.CEL AA665-HuEx- 1 1 40.653 trn 0.47619 0.324031 1_0-st-v2-01- 1_744.CEL AA612-HuEx- 1 0 22.8988 trn 0.714286 0.879576 1_0-st-v2-01- 1_750.CEL AA720-HuEx- 1 1 87.5981 trn 0.47619 NA 1_0-st-v2-01- 1_752.CEL AA613-HuEx- 1 0 20.9361 trn 0.095238 0.038548 1_0-st-v2-01- 1_753.CEL AA614-HuEx- 1 0 25.7961 trn 0.666667 0.571351 1_0-st-v2-01- 1_767.CEL AA615-HuEx- 1 1 90.9451 trn 0.571429 0.810524 1_0-st-v2-01- 1_781.CEL AA723-HuEx- 1 1 84.859 trn 0.619048 0.886662 1_0-st-v2-01- 1_816.CEL AA724-HuEx- 1 0 27.8093 trn 0.714286 NA 1_0-st-v2-01- 1_822.CEL AA668-HuEx- 1 0 24.3978 trn 0.380952 0.696265 1_0-st-v2-01- 1_827.CEL AA669-HuEx- 1 1 33.7277 trn 0.285714 0.213972 1_0-st-v2-01- 1_828.CEL AA670-HuEx- 1 0 29.3556 trn 0.333333 0.680285 1_0-st-v2-01- 1_832.CEL AA616-HuEx- 1 0 22.0787 trn 0.590909 NA 1_0-st-v2-01- 1_842.CEL AA671-HuEx- 1 1 49.7328 trn 0.333333 0.621067 1_0-st-v2-01- 1_844.CEL AA725-HuEx- 1 0 26.3833 trn 0.238095 0.166436 1_0-st-v2-01- 1_846.CEL AA672-HuEx- 1 1 42.364 trn 0.714286 NA 1_0-st-v2-01- 1_850.CEL AA617-HuEx- 0 0 19.5706 trn 0.333333 0.190879 1_0-st-v2-01- 1_852.CEL AA673-HuEx- 1 1 81.4528 trn 0.333333 0.277928 1_0-st-v2-01- 1_857.CEL AA618-HuEx- 1 1 44.3887 trn 0.619048 0.89167 1_0-st-v2-01- 1_869.CEL AA726-HuEx- 1 1 87.5466 trn 0.619048 0.585047 1_0-st-v2-01- 1_872.CEL AA674-HuEx- 1 0 29.6863 trn 0.619048 0.45009 1_0-st-v2-01- 1_877.CEL AA675-HuEx- 1 1 85.1356 trn 0.285714 0.199704 1_0-st-v2-01- 1_878.CEL AA727-HuEx- 1 1 87.4076 trn 0.380952 0.145711 1_0-st-v2-01- 1_892.CEL AA620-HuEx- 1 0 20.6594 trn 0.714286 0.651713 1_0-st-v2-01- 1_894.CEL AA728-HuEx- 0 0 16.4462 trn 0.428571 0.590833 1_0-st-v2-01- 1_895.CEL AA621-HuEx- 1 1 81.6921 trn 0.761905 0.911419 1_0-st-v2-01- 1_902.CEL AA676-HuEx- 1 1 87.3924 trn 0.380952 0.376774 1_0-st-v2-01- 1_906.CEL AA622-HuEx- 0 0 16.2054 trn 0.238095 0.137093 1_0-st-v2-01- 1_907.CEL AA677-HuEx- 1 0 29.8555 trn 0.363636 0.500079 1_0-st-v2-01- 1_911.CEL AA678-HuEx- 1 0 23.862 trn 0.380952 0.62662 1_0-st-v2-01- 1_914.CEL AA729-HuEx- 1 1 36.9093 trn 0.380952 0.145711 1_0-st-v2-01- 1_916.CEL AA623-HuEx- 1 0 27.7507 trn 0.285714 0.157834 1_0-st-v2-01- 1_924.CEL AA730-HuEx- 0 0 19.4855 trn 0.285714 0.081287 1_0-st-v2-01- 1_926.CEL AA731-HuEx- 1 0 25.638 trn 0.380952 0.599106 1_0-st-v2-01- 1_928.CEL AA679-HuEx- 1 0 22.014 trn 0.52381 0.560748 1_0-st-v2-01- 1_932.CEL AA624-HuEx- 1 1 87.6194 trn 0.380952 0.135211 1_0-st-v2-01- 1_951.CEL AA681-HuEx- 1 1 38.9953 trn 0.333333 0.703477 1_0-st-v2-01- 1_961.CEL AA626-HuEx- 1 1 38.754 trn 0.272727 0.272899 1_0-st-v2-01- 1_963.CEL AA734-HuEx- 1 1 33.0472 trn 0.333333 0.314901 1_0-st-v2-01- 1_968.CEL AA841-HuEx- 1 1 43.1947 trn 0.52381 0.29201 1_0-st-v2-01- 1_983.CEL AA735-HuEx- 1 1 38.5059 trn 0.190476 NA 1_0-st-v2-01- 1_887-A.CEL AA790-HuEx- 0 0 16.4732 tst 0.619048 0.606886 1_0-st-v2-01- 1_122.CEL AA737-HuEx- 0 0 19.4884 tst 0.666667 0.789306 1_0-st-v2-01- 1_155.CEL AA738-HuEx- 1 0 24.3258 tst 0.333333 0.142789 1_0-st-v2-01- 1_163.CEL AA740-HuEx- 0 0 18.8438 tst 0.47619 0.71363 1_0-st-v2-01- 1_168.CEL AA741-HuEx- 1 1 90.9373 tst 0.333333 0.10524 1_0-st-v2-01- 1_182.CEL AA742-HuEx- 0 0 16.0158 tst 0.47619 0.71363 1_0-st-v2-01- 1_219.CEL AA743-HuEx- 1 0 27.8375 tst 0.285714 0.273203 1_0-st-v2-01- 1_238.CEL AA744-HuEx- 0 0 16.0941 tst 0.714286 0.918596 1_0-st-v2-01- 1_240.CEL AA745-HuEx- 1 0 25.5354 tst 0.428571 0.625371 1_0-st-v2-01- 1_252.CEL AA792-HuEx- 0 0 20.2035 tst 0.47619 0.221437 1_0-st-v2-01- 1_276.CEL AA857-HuEx- 1 1 38.0538 tst 0.47619 0.418125 1_0-st-v2-01- 1_280.CEL AA747-HuEx- 1 0 27.0322 tst 0.333333 0.213076 1_0-st-v2-01- 1_306.CEL AA748-HuEx- 1 0 20.3642 tst 0.333333 0.135836 1_0-st-v2-01- 1_318.CEL AA794-HuEx- 0 0 16.2502 tst 0.238095 0.098265 1_0-st-v2-01- 1_337.CEL AA749-HuEx- 1 1 42.8271 tst 0.380952 0.325613 1_0-st-v2-01- 1_341.CEL AA751-HuEx- 0 0 18.6045 tst 0.619048 0.448316 1_0-st-v2-01- 1_352.CEL AA752-HuEx- 1 1 31.3183 tst 0.52381 0.246448 1_0-st-v2-01- 1_354.CEL AA795-HuEx- 0 0 28.1588 tst 0.666667 0.849764 1_0-st-v2-01- 1_377.CEL AA754-HuEx- 0 0 16.18 tst 0.333333 0.307941 1_0-st-v2-01- 1_387.CEL AA756-HuEx- 1 1 38.3485 tst 0.285714 0.3216 1_0-st-v2-01- 1_397.CEL AA757-HuEx- 0 0 17.2699 tst 0.333333 0.208867 1_0-st-v2-01- 1_403.CEL AA796-HuEx- 1 0 27.0163 tst 0.238095 0.127122 1_0-st-v2-01- 1_411.CEL AA797-HuEx- 1 1 31.3986 tst 0.47619 0.202085 1_0-st-v2-01- 1_412.CEL AA758-HuEx- 1 1 40.5702 tst 0.333333 0.165438 1_0-st-v2-01- 1_419.CEL AA799-HuEx- 1 1 33.0817 tst 0.428571 0.671596 1_0-st-v2-01- 1_423.CEL AA800-HuEx- 1 1 36.4681 tst 0.238095 0.191995 1_0-st-v2-01- 1_431.CEL AA759-HuEx- 1 0 27.702 tst 0.52381 0.268618 1_0-st-v2-01- 1_445.CEL AA801-HuEx- 1 1 83.1966 tst 0.52381 NA 1_0-st-v2-01- 1_458.CEL AA760-HuEx- 1 0 26.771 tst 0.428571 0.433048 1_0-st-v2-01- 1_459.CEL AA761-HuEx- 1 0 21.3604 tst 0.47619 NA 1_0-st-v2-01- 1_467.CEL AA764-HuEx- 0 0 19.7612 tst 0.190476 0.225144 1_0-st-v2-01- 1_508.CEL AA803-HuEx- 1 1 87.0205 tst 0.428571 0.293503 1_0-st-v2-01- 1_522.CEL AA765-HuEx- 0 0 17.5484 tst 0.571429 0.839756 1_0-st-v2-01- 1_557.CEL AA804-HuEx- 1 0 26.8291 tst 0.761905 0.938769 1_0-st-v2-01- 1_558.CEL AA768-HuEx- 0 0 17.0565 tst 0.52381 0.262959 1_0-st-v2-01- 1_618.CEL AA769-HuEx- 1 0 21.1354 tst 0.285714 0.29731 1_0-st-v2-01- 1_622.CEL AA807-HuEx- 1 0 29.0506 tst 0.809524 0.877799 1_0-st-v2-01- 1_641.CEL AA770-HuEx- 1 0 29.6962 tst 0.190476 0.376383 1_0-st-v2-01- 1_649.CEL AA773-HuEx- 1 0 21.4004 tst 0.380952 0.599106 1_0-st-v2-01- 1_665.CEL AA775-HuEx- 1 0 26.7034 tst 0.47619 0.676794 1_0-st-v2-01- 1_676.CEL AA809-HuEx- 0 0 17.675 tst 0.52381 0.251874 1_0-st-v2-01- 1_685.CEL AA810-HuEx- 1 1 87.4839 tst 0.47619 0.375953 1_0-st-v2-01- 1_690.CEL AA852-HuEx- 0 0 18.8276 tst 0.333333 NA 1_0-st-v2-01- 1_695.CEL AA811-HuEx- 1 0 22.2554 tst 0.47619 0.729893 1_0-st-v2-01- 1_707.CEL AA777-HuEx- 0 0 16.3117 tst 0.666667 0.477605 1_0-st-v2-01- 1_713.CEL AA778-HuEx- 0 0 15.5155 tst 0.52381 0.884073 1_0-st-v2-01- 1_716.CEL

TABLE 15 Probe set ID Category Gene Symbol 3337703 CODING PPP6R3 3326487 CODING EHF 3160006 CODING SMARCA2 3576730 CODING TC2N 2365991 CODING MPZL1 3536951 CODING KTN1 3147328 CODING UBR5 2852379 CODING ZFR 3331573 CODING CTNND1 3463598 CODING PPP1R12A 2703240 CODING KPNA4 3974728 CODING USP9X 3887661 CODING NCOA3 2758874 CODING CYTL1 2823854 CODING WDR36 2975719 CODING BCLAF1 2458376 CODING PARP1; ENAH 3754530 CODING ACACA 3757658 CODING KAT2A 3659319 CODING LONP2 3463528 CODING PAWR 2799051 CODING SLC6A19 2554001 CODING PNPT1 3012438 CODING AKAP9 4024378 CODING CDR1 3165799 CODING IFT74 2555411 CODING USP34 3536996 CODING KTN1 2669750 CODING SCN10A 3148620 CODING EIF3E 3851902 NON_CODING (CDS_ANTISENSE) CALR 2651515 NON_CODING (CDS_ANTISENSE) MECOM 3111306 NON_CODING (CDS_ANTISENSE) RSPO2 2669316 NON_CODING (CDS_ANTISENSE) GOLGA4 3560055 NON_CODING (CDS_ANTISENSE) AKAP6 3484750 NON_CODING (CDS_ANTISENSE) N4BP2L2 2651521 NON_CODING (CDS_ANTISENSE) MECOM 3002694 NON_CODING (INTRONIC) EGFR 3384586 NON_CODING (INTRONIC) DLG2 3986003 NON_CODING (INTRONIC) IL1RAPL2 3476549 NON_CODING (INTRONIC) NCOR2 3875037 NON_CODING (INTRONIC) RP5-828H9.1 3524631 NON_CODING (INTRONIC) ARGLU1 3384580 NON_CODING (INTRONIC) DLG2 3932938 NON_CODING (INTRONIC) TMPRSS3; AL773572.7 3581867 NON_CODING (INTRONIC) IGHG3 3253347 NON_CODING (ncTRANSCRIPT) RP11-428P16.2 2956494 NON_CODING (ncTRANSCRIPT) CYP2AC1P 2705151 NON_CODING (UTR) RPL22L1 3666869 NON_CODING (UTR) NFAT5 2318755 NON_CODING (UTR) PARK7 3969511 NON_CODING (UTR) OFD1 3719123 NON_CODING (UTR) ZNHIT3 3421223 NON_CODING (UTR) NUP107 3739125 NON_CODING (UTR) FN3KRP 2553585 NON_CODING (UTR) RTN4 2405285 NON_CODING (UTR) TMEM54 2473624 NON_CODING (UTR) RAB10 3593171 NON_CODING (UTR) DUT 2663553 NON_CODING (UTR) NUP210 2874688 NON_CODING (UTR) HINT1 3628924 NON_CODING (UTR) FAM96A 3066770 NON_CODING (UTR) SYPL1 3936897 NON_CODING (UTR) MRPL40 3505453 NON_CODING (UTR) MIPEP 3368555 NON_CODING (UTR) CSTF3 3985635 NON_CODING (UTR) TCEAL4 3816402 NON_CODING (UTR) OAZ1 2361095 NON_CODING (UTR) MSTO1; RP11-243J18.3; DAP3 2451873 NON_CODING (UTR) ETNK2 2414960 NON_CODING (UTR) TACSTD2 3005357 NON_CODING (UTR) CRCP 3776446 NON_CODING (UTR) MYL12A 3260965 NON_CODING (UTR) LZTS2 3619236 NON_CODING (UTR) BMF 3454547 NON_CODING (UTR_ANTISENSE) METTL7A 2735017 NON_CODING (UTR_ANTISENSE) SPARCL1 3061144 NON_CODING (UTR_ANTISENSE) ANKIB1 2710217 NON_CODING (UTR_ANTISENSE) LPP 3005652 NON_CODING (UTR_ANTISENSE) GS1-124K5.12 3854371 NON_CODING (UTR_ANTISENSE) MRPL34 3337703 CODING PPP6R3

TABLE 16 Machine Learning Feature Selection Standardization AUC AUC Algorithm Method # Features Selected Method Training Testing Na

ve Bayes Ranking based on Top 20 Percentile 0.81 0.73 (NB) Median Fold Rank Difference K-Nearest Ranking based on Top 12 Z-score 0.72 0.73 Neighbours Median Fold (KNN) Difference and Random Forest-based Gini Importance Generalized Ranking by Area 2 based on random none 0.77 0.74 Linear Under the ROC curve selection within the Model (AUC) top 100 (GLM) N.A. Ranking by Area 1 based on random none 0.69 0.71 Under the ROC curve selection within the (AUC) top 100

TABLE 17 SEQ ID NO.: Probe set ID Gene Classifier(s) Chromosome Start End Strand 353 3337703 PPP6R3 NB20 chr11 68355451 68355475 1 354 3326487 EHF KNN12, NB20 chr11 34673110 34673157 1 355 3160006 SMARCA2 KNN12, NB20 chr9 2073575 2073599 1 356 3576730 TC2N NB20 chr14 92278706 92278866 −1 357 2365991 MPZL1 NB20 chr1 167757129 167757158 1 358 3536951 KTN1 NB20 chr14 56108443 56108473 1 359 3147328 UBR5 NB20 chr8 103269860 103269932 −1 360 2852379 ZFR NB20 chr5 32417753 32417779 −1 361 3331573 CTNND1 KNN12, NB20 chr11 57577586 57577659 1 366 2758874 CYTL1 KNN12 chr4 5016922 5016946 −1 369 2458376 ENAH KNN12 chr1 225692693 225692726 −1 383 3851902 CALR NB20 chr19 13050901 13050963 −1 384 2651515 MECOM NB20 chr3 169003654 169003734 1 385 3111306 RSPO2 NB20 chr8 109084359 109084383 1 387 3560055 AKAP6 KNN12 chr14 32985209 32985233 −1 390 2886458 chr5-: KNN12, NB20 chr5 168794202 168794226 −1 168794202-168794226 391 2537212 chr2-: 343842-343866 NB20 chr2 343842 343866 −1 397 3002694 EGFR NB20 chr7 55163823 55163847 1 398 3384586 DLG2 KNN12, NB20 chr11 83467292 83467316 −1 399 3986003 IL1RAPL2 KNN12, NB20 chrX 104682956 104682980 1 410 3666869 NFAT5 NB20 chr16 69738402 69738519 1 411 2318755 PARK7 NB20 chr1 8045210 8045305 1 421 2874688 HINT1 KNN12 chr5 130495094 130495120 −1 422 3628924 FAM96A KNN12 chr15 64364822 64365114 −1 434 3260965 LZTS2 KNN12 chr10 102762254 102762278 1 436 3454547 METTL7A NB20 chr12 51324677 51324701 −1 458 2704702 MECOM SINGLE_PSR, chr3 169245434 169245479 −1 GLM2 459 3286471 HNRNPA3P1 GLM2 chr10 44285533 44285567 −1

TABLE 18 Machine Learning Feature Selection # Features Standardization AUC AUC Algorithm Method Selected Method Training Testing Support Vector Ranking by Area Top 20 None 0.95 0.75 Machine (SVM) Under the ROC curve (AUC) Support Vector Ranking by Area Top 11 None 0.96 0.8 Machine (SVM) Under the ROC curve (AUC) Support Vector Ranking by Area Top 5 None 0.98 0.78 Machine (SVM) Under the ROC curve (AUC) Generalized Ranking by Area 2 based on None 0.86 0.79 Linear Model Under the ROC random (GLM) curve (AUC) selection within the top 100

TABLE 19 SEQ ID Probe set NO.: ID Gene Classifier(s) Chromosome Start End Strand 460 3648760 SHISA9 SVM11, chr16 12996183 12996441 1 SVM20 461 2461946 GNG4 SVM11, chr1 235715432 235715511 −1 SVM5, SVM20 462 2790629 FGA SVM11, chr4 155505296 155505833 −1 SVM5, SVM20 463 3074872 PTN SVM11, chr7 136935982 136936125 −1 SVM5, SVM20 464 3558478 STXBP6 SVM11, chr14 25443877 25444024 −1 SVM5, SVM20 465 2420621 LPAR3 SVM20 chr1 85279570 85279820 −1 466 2914697 SH3BGRL2 SVM20 chr6 80341180 80341219 1 467 3501746 ARHGEF7 SVM20 chr13 111955366 111955393 1 468 3648824 SHISA9 SVM20 chr16 13297252 13297396 1 469 3750877 KIAA0100 SVM20 chr17 26942687 26942797 −1 470 3276127 chr10-: GLM2 chr10 7129102 7129152 −1 7129102-7129152 471 3648839 chr16+: SVM11, chr16 13333744 13333834 1 13333744-13333834 SVM20 472 3558521 STXBP6 SVM11, chr14 25349924 25350138 −1 GLM2, SVM20 473 3558522 STXBP6 SVM11, chr14 25350244 25350281 −1 SVM5, SVM20 474 2461975 GNG4 SVM20 chr1 235807028 235807056 −1 475 3648778 SHISA9 SVM20 chr16 13053399 13053481 1 476 3648792 SHISA9 SVM20 chr16 13156216 13156254 1 477 3091419 EPHX2 SVM11, chr8 27369439 27369789 1 SVM20 478 2461940 GNG4 SVM11, chr1 235711039 235711691 −1 SVM20 479 2461962 GNG4 SVM11, chr1 235758756 235758792 −1 SVM20 480 3558502 STXBP6 SVM20 chr14 25518570 25518806 −1

TABLE 20 # ICE Blocks per Comparison comparison, per Normal vs. Primary vs. Normal vs. GS6 vs BCR vs correlation threshold Primary Metastasis Metastasis GS7+ non-BCR Correlation 0.9  7675 (3580)  8853 (3503) 12978 (5785)  7864 (545) 7873 (506) Threshold 0.8 17288 (7019) 17773 (5622) 24433 (8445)  17415 (875)  17378 (1090) 0.7 27434 (8625) 29120 (6729) 44999 (10642) 28103 (1225) 28068 (1423) 0.6  46626 (11180) 50840 (8152) 71519 (14561) 49170 (1612) 48994 (2177)

TABLE 21A # ICE Blocks per comparison, per Normal versus Primary correlation CDS Intronic Intergenic Antisense All Other threshold Only Only Only Only Multigene Combinations Correlation 0.9 2310 245 34 26 33 932 Threshold 0.8 3196 586 118 96 189 2834 0.7 2677 799 249 242 430 4228 0.6 2248 1026 649 532 992 5733

TABLE 21B # ICE Blocks per comparison, per Primary versus Metastasis correlation CDS Intronic Intergenic Antisense All Other threshold Only Only Only Only Multigene Combinations Correlation 0.9 2058 253 32 28 43 1089 Threshold 0.8 2055 567 76 82 163 2679 0.7 1728 677 144 185 408 3587 0.6 1489 718 324 378 808 4435

TABLE 21C # ICE Blocks per comparison, per Primary versus Metastasis correlation CDS Intronic Intergenic Antisense All Other threshold Only Only Only Only Multigene Combinations Correlation 0.9 2058 253 32 28 43 1089 Threshold 0.8 2055 567 76 82 163 2679 0.7 1728 677 144 185 408 3587 0.6 1489 718 324 378 808 4435

TABLE 21D # ICE Blocks per comparison, per Normal versus Metastasis correlation CDS Intronic Intergenic Antisense All Other threshold Only Only Only Only Multigene Combinations Correlation 0.9 3064 386 61 46 82 2146 Threshold 0.8 2561 771 181 186 388 4358 0.7 2103 1018 486 495 956 5584 0.6 1685 1464 1125 1204 1987 7096

TABLE 21E # ICE Blocks per comparison, per GS6 versus GS7+ correlation CDS Intronic Intergenic Antisense All Other threshold Only Only Only Only Multigene Combinations Correlation 0.9 285 45 10 3 14 188 Threshold 0.8 287 77 28 16 55 412 0.7 298 126 39 41 105 616 0.6 267 147 77 89 174 858

TABLE 21F # ICE Blocks per comparison, per BCR versus Non-BCR correlation CDS Intronic Intergenic Antisense All Other threshold Only Only Only Only Multigene Combinations Correlation 0.9 213 112 11 5 11 154 Threshold 0.8 305 277 18 16 47 427 0.7 241 320 55 54 129 624 0.6 225 367 199 151 273 962

TABLE 22 ICE Category Block Wilcoxon Chromosomal # of Overlapping (Composition ID P-value Coordinates Genes Genes %) PSRs Probe Set ID(s) Block_2190 0.000002 chr14: 25325143 . . . 25326345; − 1 STXBP6; CODING 2 3558448; 3558449 (100%); Block_4398 0.000005 chr20: 52612441 . . . 52674693; − 1 BCAS1; CODING 3 3910385; 3910393; (100%); 3910394 Block_5988 0.000015 chr5: 120022459 . . . 120022612; + 1 PRR16; UTR (100%); 2 2825939; 2825940 Block_6655 0.000033 chr7: 136935982 . . . 136938338; − 1 PTN; CODING 2 3074872; 3074873 (100%); Block_5987 0.000044 chr5: 120021701 . . . 120022162; + 1 PRR16; CODING 2 2825937; 2825938 (100%); Block_331 0.000049 chr1: 169483568 . . . 169551730; − 1 F5; CODING 25 2443374; 2443375; (100%); 2443378; 2443381; 2443382; 2443383; 2443384; 2443385; 2443388; 2443389; 2443391; 2443392; 2443393; 2443395; 2443396; 2443397; 2443398; 2443399; 2443400; 2443403; 2443404; 2443405; 2443406; 2443407; 2443412 Block_7716 0.000074 chrX: 16142105 . . . 16175029; + 2 GRPR; CODING 4 3970026; 3970034; RP11- (50%); 3970036; 3970039 431J24.2; INTRONIC_AS (50%); Block_6372 0.000087 chr6: 38800098 . . . 38831738; + 1 DNAH8; CODING 13 2905993; 2905995; (100%); 2905996; 2905997; 2905999; 2906000; 2906001; 2906002; 2906003; 2906004; 2906005; 2906010; 2906012 Block_4271 0.000112 chr2: 219676945 . . . 219679977; + 1 CYP27A1; CODING 7 2528108; 2528110; (85.71%); UTR 2528111; 2528112; (14.28%); 2528113; 2528115; 2528118 Block_4397 0.000132 chr20: 52574002 . . . 52601991; − 1 BCAS1; CODING 3 3910367; 3910373; (100%); 3910378 Block_5000 0.000132 chr3: 3886073 . . . 3890904; + 2 LRRN1; INTRONIC_AS 5 2608321; 2608324; SUMF1; (40%); 2608326; 2608331; CODING 2608332 (20%); UTR (40%); Block_1039 0.00014 chr10: 43609044 . . . 43610087; + 1 RET; CODING 2 3243869; 3243870 (100%); Block_3838 0.000197 chr2: 100484261 . . . 100509150; − 1 AFF3; INTRONIC 2 2567082; 2567086 (100%); Block_7796 0.000205 chrX: 105153170 . . . 105156727; + 1 NRK; CODING 2 3986120; 3986121 (100%); Block_5986 0.000209 chr5: 119801697 . . . 119998479; + 1 PRR16; UTR (16.66%); 6 2825917; 2825921; INTRONIC 2825922; 2825923; (83.33%); 2825928; 2825932 Block_1733 0.000213 chr12: 103234188 . . . 103249107; − 1 PAH; CODING 3 3468486; 3468494; (100%); 3468504 Block_3839 0.000218 chr2: 100667261 . . . 100690911; − 1 AFF3; INTRONIC 2 2567016; 2567024 (100%); Block_6879 0.000218 chr8: 22570904 . . . 22582442; − 1 PEBP4; CODING 2 3127612; 3127614 (100%); Block_413 0.00025 chr1: 235712540 . . . 235715511; − 1 GNG4; CODING 4 2461942; 2461944; (25%); UTR 2461945; 2461946 (75%); Block_4396 0.00027 chr20: 52571654 . . . 52574704; − 1 BCAS1; INTRONIC 2 3910366; 3910368 (100%); Block_7431 0.000292 chr9: 96069125 . . . 96069401; + 1 WNK2; ncTRANSCRIPT 2 3179784; 3179785 (100%); Block_1146 0.000309 chr10: 123779283 . . . 123781483; + 1 TACC2; ncTRANSCRIPT 2 3268069; 3268071 (50%); UTR (50%); Block_7640 0.000315 chrX: 106959080 . . . 106959334; − 1 TSC22D3; CODING 2 4017408; 4017410 (50%); UTR (50%); Block_6371 0.000328 chr6: 38783258 . . . 38783411; + 1 DNAH8; CODING 2 2905985; 2905986 (100%); Block_1735 0.000361 chr12: 103306570 . . . 103306674; − 1 PAH; CODING 2 3468531; 4053738 (100%); Block_4308 0.000428 chr2: 242135147 . . . 242164581; + 1 ANO7; CODING 24 2536222; 2536226; (91.66%); UTR 2536228; 2536229; (8.33%); 2536231; 2536232; 2536233; 2536234; 2536235; 2536236; 2536237; 2536238; 2536240; 2536241; 2536243; 2536245; 2536248; 2536249; 2536252; 2536253; 2536256; 2536260; 2536261; 2536262 Block_3836 0.000436 chr2: 100377851 . . . 100400837; − 1 AFF3; INTRONIC 2 2566945; 2566952 (100%); Block_6570 0.000497 chr7: 37946647 . . . 37956059; − 1 SFRP4; CODING 9 3046448; 3046449; (66.66%); UTR 3046450; 3046457; (33.33%); 3046459; 3046460; 3046461; 3046462; 3046465 Block_1532 0.000507 chr11: 114311909 . . . 114320545; + 1 REXO2; CODING 6 3349958; 3349959; (33.33%); 3349966; 3349970; INTRONIC 3349975; 3349979 (66.66%); Block_2087 0.000507 chr13: 24464154 . . . 24465613; + 1 RP11- ncTRANSCRIPT 2 3481518; 3481519 45B20.3; (100%); Block_2922 0.000536 chr16: 81047741 . . . 81065037; + 1 CENPN; CODING 10 3670638; 3670639; (80%); UTR 3670641; 3670644; (10%); 3670645; 3670650; INTRONIC 3670659; 3670660; (10%); 3670661; 3670666 Block_3281 0.000588 chr17: 65027167 . . . 65028692; + 2 CACNG4; CODING 2 3732138; 3732139 AC005544.1; (50%); UTR (50%); Block_5080 0.000657 chr3: 53528861 . . . 53847736; + 1 CACNA1D; ncTRANSCRIPT 91 2624389; 2624393; (1.09%); 2624394; 2624395; CODING 2624397; 2624398; (49.45%); UTR 2624399; 2624400; (2.19%); 2624401; 2624402; INTRONIC 2624403; 2624404; (47.25%); 2624405; 2624406; 2624407; 2624408; 2624529; 2624531; 2624533; 2624537; 2624411; 2624412; 2624413; 2624415; 2624416; 2624417; 2624421; 2624422; 2624424; 2624426; 2624427; 2624428; 2624429; 2624430; 2624432; 2624434; 2624435; 2624438; 2624439; 2624440; 2624441; 2624442; 2624443; 2624444; 2624446; 2624453; 2624458; 2624459; 2624460; 2624461; 2624462; 2624465; 2624466; 2624467; 2624470; 2624472; 2624473; 2624475; 2624477; 2624479; 2624480; 2624481; 2624482; 2624484; 2624485; 2624487; 2624488; 2624490; 2624491; 2624492; 2624493; 2624494; 2624495; 2624496; 2624499; 2624500; 2624501; 2624502; 2624503; 2624504; 2624505; 2624507; 2624508; 2624511; 2624512; 2624515; 2624516; 2624518; 2624519; 2624526; 2624527 Block_6033 0.000669 chr5: 149357733 . . . 149361471; + 1 SLC26A2; CODING 2 2835310; 2835314 (50%); UTR (50%); Block_1566 0.000733 chr11: 129722378 . . . 129729817; + 1 TMEM45B; CODING 7 3356054; 3356055; (85.71%); UTR 3356056; 3356058; (14.28%); 3356061; 3356063; 3356066 Block_1222 0.000746 chr11: 30601825 . . . 30602041; − 1 MPPED2; CODING 2 3367741; 3367743 (50%); UTR (50%); Block_2090 0.00076 chr13: 26145795 . . . 26156094; + 1 ATP8A2; CODING 3 3482326; 3482335; (100%); 3482336 Block_4334 0.000774 chr20: 10619700 . . . 10620579; − 1 JAG1; CODING 3 3897508; 3897509; (33.33%); UTR 3897512 (66.66%); Block_2162 0.000788 chr13: 111932910 . . . 111938586; + 1 ARHGEF7; CODING 2 3501728; 3501736 (100%); Block_2628 0.000788 chr15: 74005696 . . . 74005846; + 1 CD276; UTR (100%); 2 3601259; 3601260 Block_5303 0.000803 chr4: 80898781 . . . 80905088; − 1 ANTXR2; CODING 3 2775016; 2775017; (100%); 2775018; Block_213 0.000832 chr1: 85277703 . . . 85279820; − 1 LPAR3; CODING 3 2420617; 2420619; (33.33%); UTR 2420621 (66.66%); Block_773 0.000863 chr1: 220870275 . . . 220872267; + 1 C1orf115; UTR (100%); 2 2381258; 2381260 Block_3219 0.000927 chr17: 40932892 . . . 40945698; + 1 WNK4; CODING 8 3722087; 3722090; (100%); 3722094; 3722095; 3722100; 3722101; 3722105; 3722106 Block_7722 0.001069 chrX: 18643259 . . . 18646559; + 1 CDKL5; CODING 2 3970693; 3970698 (100%); Block_5415 0.001107 chr4: 170016681 . . . 170017797; − 1 SH3RF1; CODING 3 2793150; 2793151; (66.66%); UTR 2793152 (33.33%); Block_6420 0.001127 chr6: 80383340 . . . 80406282; + 1 SH3BGRL2; CODING 2 2914706; 2914708 (100%); Block_6142 0.001147 chr6: 38890758 . . . 38901026; − 1 RP1- ncTRANSCRIPT 7 2952718; 2952719; 207H1.3; (85.71%); 2952720; 2952721; INTRONIC 2952723; 2952724; (14.28%); 2952725 Block_3837 0.001188 chr2: 100426047 . . . 100692345; − 1 AFF3; CODING 61 2566957; 2566960; (6.55%); 2566961; 2566965; ncTRANSCRIPT 2566966; 2566971; (3.27%); 2567075; 2567076; INTRONIC 2567084; 2567063; (90.16%); 2566976; 2567087; 2567088; 2566977; 2567064; 2567097; 2567067; 2567069; 2567101; 2567103; 2567071; 2566979; 2566982; 2566983; 2566984; 2566985; 2567105; 2567111; 2567113; 2567115; 2567106; 2566987; 2566988; 2566991; 2566993; 2566994; 2566996; 2566997; 2567121; 2566998; 2567125; 2567000; 2567001; 2567002; 2567003; 2567005; 2567007; 2567008; 2567010; 2567011; 2567012; 2567013; 2567014; 2567015; 2567017; 2567018; 2567019; 2567020; 2567022; 2567023; 2567127 Block_1378 0.001391 chr 11: 134022950 . . . 134052868; − 1 NCAPD3; ncTRANSCRIPT 11 3399552; 3399554; (45.45%); 3399556; 3399558; INTRONIC 3399559; 3399560; (54.54%); 3399561; 3399568; 3399575; 3399578; 3399582 Block_3834 0.001415 chr2: 100199328 . . . 100318709; − 1 AFF3; CODING 22 2566873; 2566875; (22.72%); UTR 2566880; 2566885; (4.54%); 2566886; 2566888; INTRONIC 2566893; 2566898; (72.72%); 2566900; 2566902; 2566905; 2566906; 2566908; 2566910; 2566911; 2566912; 2566915; 2566919; 2566920; 2566922; 2566924; 2566929 Block_4395 0.001569 chr20: 52560335 . . . 52561534; − 1 BCAS1; CODING 2 3910362; 3910363 (50%); UTR (50%); Block_6520 0.001624 chr6: 160770298 . . . 160864773; + 2 AL591069.1; ncTRANSCRIPT 29 2934526; 2934527; SLC22A3; (3.44%); 2934531; 2934533; CODING 2934535; 2934580; (27.58%); 2934582; 2934585; INTRONIC 2934586; 2934536; (68.96%); 2934537; 2934538; 2934539; 2934541; 2934543; 2934545; 2934547; 2934548; 2934549; 2934550; 2934551; 2934554; 2934556; 2934557; 2934558; 2934559; 2934560; 2934561; 2934562 Block_3917 0.001652 chr2: 178762785 . . . 178769891; − 1 PDE11A; CODING 2 2589116; 2589118 (100%); Block_3752 0.001681 chr2: 42662806 . . . 42670619; − 1 KCNG3; INTERGENIC 2 2550177; 2550178 (50%); UTR (50%); Block_7162 0.001739 chr9: 3262938 . . . 3271101; − 1 RFX3; CODING 2 3196865; 3196873 (100%); Block_5975 0.001769 chr5: 113698875 . . . 113699698; + 1 KCNN2; CODING 2 2824632; 2824635 (100%); Block_6604 0.001769 chr7: 87907478 . . . 87920296; − 1 STEAP4; CODING 12 3060339; 3060340; (75%); UTR 3060341; 3060342; (25%); 3060343; 3060344; 3060347; 3060348; 3060350; 3060351; 3060352; 3060353 Block_4200 0.0018 chr2: 181852076 . . . 181894023; + 1 UBE2E3; CODING 5 2518175; 2518178; (20%); 2518179; 2518180; INTRONIC 2518184 (80%); Block_4201 0.0018 chr2: 181920432 . . . 181924616; + 1 UBE2E3; INTRONIC 3 2518192; 2518193; (100%); 2518197 Block_3913 0.001926 chr2: 178528594 . . . 178540212; − 1 PDE11A; CODING 2 2589038; 2589043 (100%); Block_5936 0.001926 chr5: 79361251 . . . 79378964; + 1 THBS4; CODING 10 2817602; 2817603; (100%); 2817605; 2817606; 2817609; 2817611; 2817614; 2817615; 2817620; 2817621 Block_3916 0.001959 chr2: 178681582 . . . 178705094; − 1 PDE11A; CODING 3 2589101; 2589102; (100%); 2589105 Block_4125 0.001959 chr2: 101541626 . . . 101564800; + 1 NPAS2; CODING 4 2496436; 2496440; (100%); 2496446; 2496448 Block_2925 0.001992 chr16: 84479997 . . . 84485677; + 1 ATP2C2; CODING 2 3671768; 3671774 (100%); Block_874 0.002026 chr10: 33545282 . . . 33559775; − 1 NRP1; CODING 3 3284370; 3284373; (100%); 3284377 Block_4971 0.002061 chr3: 184910469 . . . 184922544; − 1 EHHADH; CODING 3 2708726; 2708727; (100%); 2708733 Block_2216 0.002131 chr14: 51379747 . . . 51387339; − 1 PYGL; CODING 2 3564224; 3564231 (100%); Block_6886 0.002131 chr8: 27317314 . . . 27336535; − 1 CHRNA2; CODING 10 3129025; 3129030; (60%); UTR 3129034; 3129038; (40%); 3129039; 3129040; 3129044; 3129045; 3129046; 3129047 Block_1533 0.002167 chr11: 114311389 . . . 114314645; + 1 REXO2; CODING 2 3349956; 3349963 (100%); Block 4336 0.002167 chr20: 10632779 . . . 10644662; − 1 JAG1; CODING 4 3897552; 3897558; (100%); 3897559; 3897568 Block_4349 0.002167 chr20: 20596706 . . . 20621488; − 1 RALGAPA2; CODING 5 3900218; 3900220; (100%); 3900228; 3900233; 3900235 Block_1576 0.002204 chr11: 134147231 . . . 134188819; + 1 GLB1L3; CODING 13 3357348; 3357349; (100%); 3357360; 3357363; 3357369; 3357370; 3357371; 3357375; 3357382; 3357383; 3357384; 3357386; 3357387 Block_3611 0.002204 chr19: 32080316 . . . 32084433; + 0 INTERGENIC 2 3828710; 3828717 (100%); Block_1649 0.002241 chr12: 44913789 . . . 44915959; − 1 NELL2; CODING 2 3451835; 3451838 (100%); Block_5976 0.002317 chr5: 113740155 . . . 113740553; + 1 KCNN2; CODING 2 2824643; 2824644 (100%); Block_1964 0.002476 chr12: 121134218 . . . 121137627; + 1 MLEC; CODING 2 3434542; 3434546 (50%); UTR (50%); Block_2762 0.002476 chr16: 56701878 . . . 56701935; − 1 MT1G; CODING 2 3693007; 3693008 (50%); UTR (50%); Block_4864 0.002476 chr3: 116058173 . . . 116094106; − 1 LSAMP; INTRONIC 3 2690112; 2690113; (100%); 2690118 Block_829 0.002559 chr1: 247712494 . . . 247739511; + 1 C1orf150; CODING 3 2390125; 2390128; (66.66%); UTR 2390134 (33.33%); Block_2311 0.002602 chr14: 38054451 . . . 38055847; + 0 INTERGENIC 4 3533031; 3533035; (100%); 3533037; 3533039 Block_2822 0.002602 chr16: 8875186 . . . 8878061; + 1 ABAT; CODING 5 3647480; 3647481; (20%); UTR 3647483; 3647484; (80%); 3647485 Block_5310 0.002602 chr4: 82026968 . . . 82031699; − 1 PRKG2; CODING 2 2775219; 2775221 (100%); Block_7638 0.002602 chrX: 106957270 . . . 106960029; − 1 TSC22D3; CODING 6 4017398; 4017399; (50%); UTR 4017400; 4017403; (50%); 4017409; 4017414 Block_1652 0.002645 chr12: 45168545 . . . 45173801; − 1 NELL2; CODING 4 3451885; 3451888; (100%); 3451889; 3451891 Block_1917 0.002645 chr12: 81528607 . . . 81545849; + 1 ACSS3; CODING 4 3424233; 3424234; (100%); 3424243; 3424244 Block_1933 0.002779 chr12: 102113921 . . . 102117625; + 1 CHPT1; CODING 2 3428698; 3428702 (100%); Block_3096 0.002872 chr17: 74622431 . . . 74625201; − 1 ST6GALNAC1; CODING 6 3771721; 3771722; (100%); 3771723; 3771725; 3771726; 3771727 Block_3273 0.002872 chr17: 59093209 . . . 59112144; + 1 BCAS3; CODING 2 3729624; 3729628 (100%); Block_3832 0.002872 chr2: 100165334 . . . 100170892; − 1 AFF3; CODING 4 2566847; 2566849; (50%); UTR 2566850; 2566851 (50%); Block_6032 0.002872 chr5: 149357507 . . . 149366444; + 1 SLC26A2; CODING 7 2835309; 2835311; (57.14%); UTR 2835312; 2835313; (42.85%); 2835315; 2835316; 2835317 Block_214 0.003016 chr1: 85331090 . . . 85331666; − 1 LPAR3; CODING 2 2420633; 2420635 (100%); Block_4670 0.003016 chr22: 32480910 . . . 32482314; + 1 SLC5A1; CODING 2 3943253; 3943255 (100%); Block_5621 0.003016 chr4: 159812601 . . . 159828286; + 1 FNIP2; CODING 6 2749669; 2749671; (50%); UTR 2749675; 2749676; (50%); 2749677; 2749678 Block_7835 0.003016 chrX: 152770164 . . . 152773851; + 1 BGN; CODING 6 3995642; 3995651; (100%); 3995654; 3995657; 3995659; 3995661 Block_4022 0.003115 chr2: 1718308 . . . 11721346; + 1 GREB1; UTR (50%); 2 2469846; 2469850 INTRONIC (50%); Block_6521 0.003218 chr6: 160866011 . . . 160868068; + 1 SLC22A3; INTRONIC 3 2934564; 2934565; (100%); 2934567 Block_4344 0.003271 chr20: 20475772 . . . 20507004; − 1 RALGAPA2; CODING 7 3900137; 3900143; (100%); 3900149; 3900150; 3900152; 3900154; 3900156 Block_3505 0.003324 chr19: 15297695 . . . 15302661; − 1 NOTCH3; CODING 5 3853157; 3853158; (100%); 3853159; 3853161; 3853166 Block_4335 0.003324 chr20: 10621471 . . . 10630262; − 1 JAG1; CODING 16 3897514; 3897515; (100%); 3897516; 3897517; 3897518; 3897519; 3897520; 3897527; 3897529; 3897531; 3897533; 3897535; 3897536; 3897537; 3897539; 3897540 Block_3168 0.003433 chr17: 7945688 . . . 7951882; + 1 ALOX15B; CODING 11 3709424; 3709426; (100%); 3709428; 3709429; 3709430; 3709432; 3709433; 3709435; 3709437; 3709438; 3709440 Block_456 0.003433 chr1: 19981582 . . . 19984800; + 1 NBL1; CODING 3 2323777; 2323778; (66.66%); UTR 2323782 (33.33%); Block_1377 0.003489 chr11: 134022430 . . . 134095174; − 1 NCAPD3; CODING 42 3399550; 3399551; (90.47%); UTR 3399553; 3399555; (7.14%); 3399562; 3399563; INTRONIC 3399565; 3399566; (2.38%); 3399567; 3399569; 3399570; 3399571; 3399572; 3399573; 3399574; 3399576; 3399577; 3399579; 3399580; 3399581; 3399583; 3399584; 3399585; 3399587; 3399588; 3399589; 3399590; 3399591; 3399592; 3399593; 3399594; 3399595; 3399597; 3399598; 3399600; 3399601; 3399602; 3399603; 3399605; 3399606; 3399607; 3399613 Block_1505 0.003545 chr11: 92085296 . . . 92088273; + 1 FAT3; CODING 3 3344438; 3344439; (100%); 3344440 Block_4671 0.003545 chr22: 32498039 . . . 32507284; + 1 SLC5A1; CODING 5 3943258; 3943259; (60%); UTR 3943261; 3943263; (40%); 3943265 Block_743 0.003603 chr1: 203275102 . . . 203275613; + 1 BTG2; INTRONIC 3 2375667; 2375668; (100%); 2375670 Block_4306 0.003661 chr2: 241404507 . . . 241405065; + 1 GPC1; CODING 2 2535800; 2535802 (100%); Block_6592 0.003661 chr7: 80546027 . . . 80548317; − 1 SEMA3C; CODING 2 3058814; 3058816 (50%); UTR (50%); Block_4345 0.003841 chr20: 20486102 . . . 20517400; − 1 RALGAPA2; CODING 5 3900146; 3900151; (100%); 3900155; 3900164; 3900167 Block_1651 0.003902 chr12: 45059307 . . . 45097550; − 1 NELL2; CODING 2 3451868; 3451874 (100%); Block_7859 0.003902 chrY: 14799855 . . . 14802344; + 1 TTTY15; ncTRANSCRIPT 2 4030072; 4030074 (100%); Block_2091 0.003965 chr13: 26411312 . . . 26434996; + 1 ATP8A2; CODING 2 3482379; 3482386 (100%); Block_4935 0.003965 chr3: 142567065 . . . 142567284; − 1 PCOLCE2; CODING 2 2699027; 2699028 (100%); Block_1366 0.004093 chr11: 124617431 . . . 124619754; − 1 VSIG2; CODING 2 3396086; 3396095 (100%); Block_1999 0.004093 chr13: 38158866 . . . 38162106; − 1 POSTN; CODING 2 3510099; 3510102 (100%); Block_2897 0.004158 chr16: 67202953 . . . 67203210; + 1 HSF4; CODING 2 3665255; 3665257; (100%); Block_3442 0.004292 chr18: 56585564 . . . 56587447; + 1 ZNF532; CODING 3 3790379; 3790380; (100%); 3790381 Block_5409 0.004429 chr4: 159046177 . . . 159048546; − 1 FAM198B; UTR (100%); 4 2791422; 2791423; 2791424; 2791425 Block_6505 0.004429 chr7: 87910829 . . . 87912896; − 1 STEAP4; UTR (50%); 2 3060345; 3060349 INTRONIC (50%); Block_7860 0.004429 chrY: 14838600 . . . 14968421; + 1 USP9Y; CODING 18 4030087; 4030096; (100%); 4030104; 4030112; 4030113; 4030115; 4030116; 4030119; 4030120; 4030125; 4030126; 4030127; 4030128; 4030134; 4030144; 4030146; 4030149; 4030153 Block_873 0.004429 chr10: 33491851 . . . 33515213; − 1 NRP1; CODING 4 3284334; 3284341; (100%); 3284346; 3284351 Block_1221 0.004499 chr11: 30443973 . . . 30517053; − 1 MPPED2; CODING 12 3367684; 3367688; (16.66%); 3367691; 3367693; ncTRANSCRIPT 3367696; 3367697; (8.33%); 3367702; 3367706; INTRONIC 3367707; 3367710; (75%) 3367712; 3367714 Block_3512 0.004499 chr19: 18893864 . . . 18897074; − 1 COMP; CODING 2 3855221; 3855230 (100%); Block_3914 0.00457 chr2: 178565861 . . . 178592888; − 1 PDE11A; CODING 4 2589055; 2589058; (100%); 2589064; 2589065 Block_5309 0.00457 chr4: 80992745 . . . 80993659; − 1 ANTXR2; CODING 2 2775042; 2775043 (100%); Block_3446 0.004716 chr18: 56819806 . . . 56824879; + 1 SEC11C; CODING 2 3790485; 3790494 (100%); Block_453 0.004716 chr1: 16332765 . . . 16333026; + 1 C1orf64; CODING 2 2322216; 2322218 (50%); UTR (50%); Block_169 0.00479 chr1: 53373542 . . . 53377448; − 1 ECHDC2; CODING 2 2413055; 2413058 (100%); Block_3443 0.00479 chr18: 56623078 . . . 56648694; + 1 ZNF532; INTRONIC 6 3790396; 3790398; (100%); 3790399; 3790401; 3790403; 3790404 Block_5081 0.00479 chr3: 53736689 . . . 53753808; + 1 CACNA1D; CODING 3 2624448; 2624454; (100%); 2624457 Block_4829 0.004865 chr3: 86988621 . . . 87039865; − 1 VGLL3; CODING 17 2684857; 2684831; (58.82%); UTR 2684832; 2684833; (41.17%); 2684835; 2684859; 2684861; 2684863; 2684865; 2684867; 2684869; 2684871; 2684873; 2684877; 2684879; 2684881; 2684883 Block_886 0.004942 chr10: 61551607 . . . 61572483; − 1 CCDC6; CODING 7 3290791; 3290792; (71.42%); UTR 3290796; 3290799; (28.57%); 3290802; 3290803; 3290807 Block_1330 0.005019 chr11: 106555201 . . . 106558073; − 1 GUCY1A2; UTR (100%); 2 3389670; 3389672 Block_3835 0.005019 chr2: 100372047 . . . 100415240; − 1 AFF3; INTRONIC 5 2566941; 2566942; (100%); 2566948; 2566949; 2566955 Block_481 0.005019 chr1: 27676149 . . . 27677810; + 1 SYTL1; CODING 3 2327014; 2327022; (100%); 2327025 Block_3688 0.005098 chr19: 55315113 . . . 55315146; + 1 KIR2DL4; CODING 2 3841790; 4052980 (100%); Block_4342 0.005098 chr20: 20370667 . . . 20373784; − 1 RALGAPA2; CODING 3 3900089; 3900090; (33.33%); UTR 3900092 (66.66%); Block_6457 0.005098 chr6: 138657744 . . . 138658255; + 1 KIAA1244; UTR (100%); 3 2927694; 2927695; 2927696 Block_5167 0.005178 chr3: 156170688 . . . 156192603; + 1 KCNAB1; CODING 3 2649038; 2649044; (100%); 2649051 Block_5620 0.005178 chr4: 159772477 . . . 159790535; + 1 FNIP2; CODING 9 2749639; 2749640; (100%); 2749644; 2749646; 2749647; 2749648; 2749650; 2749651; 2749652 Block_1250 0.005258 chr11: 61290559 . . . 61291972; − 1 SYT7; CODING 3 3375403; 3375404; (100%); 3375405; Block_2089 0.005258 chr13: 26104137 . . . 26163815; + 1 ATP8A2; CODING 12 3482305; 3482309; (100%); 3482310; 3482313; 3482314; 3482316; 3482319; 3482321; 3482322; 3482330; 3482333; 3482337 Block_3915 0.005258 chr2: 178621229 . . . 178630397; − 1 PDE11A; INTRONIC 3 2589079; 2589083; (100%); 2589089 Block_5060 0.00534 chr3: 48289117 . . . 48312089; + 1 ZNF589; CODING 9 2621590; 2621598; (55.55%); UTR 2621602; 2621603; (44.44%); 2621604; 2621606; 2621607; 2621608; 2621609 Block_5619 0.00534 chr4: 159750328 . . . 159754780; + 1 FNIP2; CODING 4 2749625; 2749626; (100%); 2749627; 2749629 Block_2896 0.005508 chr16: 67199438 . . . 67201057; + 1 HSF4; ncTRANSCRIPT 5 3665235; 3665240; (20%); 3665244; 3665245; CODING 3665246 (80%); Block_3964 0.005508 chr2: 204309603 . . . 204313496; − 1 RAPH1; CODING 3 2595578; 2595581; (100%); 2595583 Block_4025 0.00568 chr2: 13872471 . . . 13926374; + 2 NCRNA00276; INTRONIC 2 2470336; 2470352 AC016730.1; (50%); INTRONIC_AS (50%); Block_4861 0.00568 chr3: 115524258 . . . 115529246; − 1 LSAMP; CODING 5 2690021; 2690022; (20%); 2690023; 2690025; INTERGENIC 2690027 (60%); UTR (20%); Block_1220 0.005947 chr11: 30431953 . . . 30439165; − 1 MPPED2; CODING 4 3367675; 3367676; (75%); UTR 3367679; 3367680 (25%); Block_736 0.005947 chr1: 201285703 . . . 201293641; + 1 PKP1; CODING 4 2374622; 2374628; (100%); 2374629; 2374631 Block_7442 0.005947 chr9: 101589035 . . . 101611356; + 1 GALNT12; CODING 5 3181611; 3181614; (100%); 3181620; 3181622; 3181628; Block_7533 0.005947 chrX: 1505524 . . . 1506210; − 1 SLC25A6; CODING 3 3997378; 4033179; (100%); 4033181 Block_1251 0.006039 chr11: 61295389 . . . 61300540; − 1 SYT7; CODING 2 3375406; 3375409 (100%); Block_3169 0.006039 chr17: 7960222 . . . 7966722; + 0 INTERGENIC 6 3709445; 3709446; (100%); 3709448; 3709451; 3709453; 3709455 Block_3903 0.006039 chr2: 169094505 . . . 169097430; − 1 STK39; INTRONIC 2 2585794; 2585796 (100%); Block_1997 0.006132 chr13: 38154719 . . . 38164537; − 1 POSTN; CODING 3 3510096; 3510097; (100%); 3510103 Block_4863 0.006226 chr3: 115984267 . . . 116001005; − 1 LSAMP; INTRONIC 3 2690278; 2690273; (100%); 2690288 Block_7046 0.006226 chr8: 27358443 . . . 27380016; + 1 EPHX2; CODING 6 3091408; 3091410; (100%); 3091412; 3091414; 3091418; 3091427 Block_3912 0.006321 chr2: 178493807 . . . 178494276; − 1 PDE11A; CODING 2 2589025; 2589028 (50%); UTR (50%); Block_4194 0.006321 chr2: 173885368 . . . 173891966; + 1 RAPGEF4; CODING 2 2515897; 2515902 (100%); Block_4979 0.006418 chr3: 189674965 . . . 189681873; − 1 LEPREL1; CODING 4 2710476; 2710477; (75%); UTR 2710483; 2710484 (25%); Block_5977 0.006615 chr5: 113798749 . . . 113808838; + 1 KCNN2; CODING 3 2824655; 2824656; (100%); 2824657 Block_1534 0.006818 chr11: 114315278 . . . 114320629; + 1 REXO2; CODING 3 3349968; 3349972; (100%); 3349980 Block_4346 0.006818 chr20: 20552104 . . . 20563856; − 1 RALGAPA2; CODING 3 3900185; 3900187; (100%); 3900191 Block_1731 0.006922 chr12: 102173985 . . . 102190536; − 1 GNPTAB; CODING 3 3468148; 3468152; (100%); 3468159; Block_4980 0.006922 chr3: 189689680 . . . 189713231; − 1 LEPREL1; CODING 12 2710494; 2710495; (100%); 2710496; 2710498; 2710502; 2710503; 2710504; 2710505; 2710506; 2710509; 2710510; 2710511 Block_5661 0.007241 chr5: 29476852 . . . 29477004; − 0 INTERGENIC 2 2851724; 2851725 (100%); Block_7425 0.007241 chr9: 90301466 . . . 90312118; + 1 DAPK1; CODING 2 3177954; 3177956 (100%); Block_5416 0.00735 chr4: 170037444 . . . 170043285; − 1 SH3RF1; CODING 3 2793155; 2793156; (100%); 2793159 Block_5567 0.007461 chr4: 108866136 . . . 108873298; + 1 CYP2U1; CODING 6 2738706; 2738707; (66.66%); UTR 2738708; 2738712; (33.33%); 2738714; 2738715 Block_6698 0.007461 chr7: 12620691 . . . 12691507; + 1 SCIN; CODING 9 2990415; 2990418; (100%); 2990420; 2990421; 2990424; 2990425; 2990427; 2990430; 2990431 Block_4865 0.007573 chr3: 116123466 . . . 116161481; − 1 LSAMP; INTRONIC 5 2690300; 2690302; (100%); 2690304; 2690131; 2690132 Block_6522 0.007573 chr6: 160868751 . . . 160872088; + 1 SLC22A3; CODING 2 2934572; 2934575 (100%); Block_2419 0.007687 chr15: 23006467 . . . 23014513; − 1 NIPA2; CODING 2 3613310: 3613312 (100%); Block_6164 0.007687 chr6: 55739210 . . . 55740206; − 1 BMP5; CODING 4 2958199; 2958200; (75%); UTR 2958201; 2958202 (25%); Block_2128 0.007919 chr13: 76379046 . . . 76382387; + 1 LMO7; INTRONIC 3 3494196; 3494197; (100%); 3494206 Block_3455 0.007919 chr19: 282756 . . . 287715; − 1 PPAP2C; CODING 2 3844475; 3844477 (100%); Block_4347 0.007919 chr20: 20582326 . . . 20586044; − 1 RALGAPA2; CODING 2 3900205; 3900207 (100%); Block_5364 0.008158 chr4: 120442102 . . . 120528393; − 1 PDE5A; CODING 11 2783626; 2783629; (100%); 2783637; 2783638; 2783644; 2783650; 2783652; 2783654; 2783659; 2783662; 2783663 Block_7048 0.008279 chr8: 27398133 . . . 27402173; + 1 EPHX2; CODING 2 3091435; 3091442 (50%); UTR (50%); Block_1772 0.008528 chr12: 118470966 . . . 118480761; − 1 WSB2; CODING 5 3473729; 3473732; (80%); UTR 3473735; 3473736; (20%); 3473739 Block_3445 0.008528 chr18: 56820014 . . . 56824583; + 1 SEC11C; UTR (20%); 5 3790486; 3790487; INTRONIC 3790489; 3790492; (80%); 3790493 Block_1105 0.008654 chr10: 102732697 . . . 102737466; + 1 SEMA4G; CODING 2 3260899; 3260903 (100%); Block_1978 0.008654 chr13: 24254773 . . . 24280276; − 0 INTERGENIC 8 3505432; 3505434; (100%); 3505436; 3505438; 3505440; 3505442; 3505444; 3505446 Block_5305 0.008782 chr4: 80929675 . . . 80954689; − 1 ANTXR2; CODING 3 2775023; 2775024; (100%); 2775031 Block_1998 0.008912 chr13: 38158126 . . . 38166301; − 1 POSTN; CODING 4 3510098; 3510100; (100%); 3510101; 3510105 Block_4944 0.008912 chr3: 148895685 . . . 148939500; − 1 CP; CODING 9 2700263; 2700272; (100%); 2700276; 2700284; 2700287; 2700288; 2700289; 2700292; 2700300; Block_3901 0.009044 chr2: 168920012 . . . 168921891; − 1 STK39; CODING 2 2585735; 2585736; (100%); Block_4348 0.009044 chr20: 20591959 . . . 20601268; − 1 RALGAPA2; CODING 3 3900211; 3900212; (100%); 3900221 Block_6149 0.009044 chr6: 46821609 . . . 46836749; − 1 GPR116; CODING 7 2955866; 2955877; (85.71%); UTR 2955879; 2955881; (14.28%); 2955884; 2955885; 2955887 Block_1274 0.009177 chr11: 65197863 . . . 65204294; − 1 NEAT1; ncTRANSCRIPT_AS 3 3377621; 3377623; (100%); 3377630 Block_1797 0.009177 chr12: 125398337 . . . 125399059; − 1 UBC; UTR (100%); 5 3476772; 3476773; 3476774; 3476775; 3476776 Block_461 0.009177 chr1: 24766662 . . . 24799256; + 1 NIPAL3; CODING 12 2325438; 2325443; (83.33%); UTR 2325444; 2325445; (16.66%); 2325448; 2325449; 2325452; 2325453; 2325457; 2325462; 2325463; 2325464 Block_5410 0.009177 chr4: 159052021 . . . 159091694; − 1 FAM198B; CODING 3 2791428; 2791433; (100%); 2791438 Block_6449 0.009177 chr6: 132190499 . . . 132196962; + 1 ENPP1; CODING 2 2925975; 2925979 (100%); Block_2924 0.009312 chr16: 84449114 . . . 84482221; + 1 ATP2C2; CODING 4 3671751; 3671757; (100%); 3671766; 3671769 Block_6700 0.009312 chr7: 16815929 . . . 16823939; + 1 TSPAN13; CODING 5 2991161; 2991163; (80%); UTR 2991164; 2991165; (20%); 2991172 Block_7016 0.009312 chr8: 424465 . . . 424757; + 0 INTERGENIC 2 3082591; 3082592; (100%); Block_6140 0.009449 chr6: 38643851 . . . 38649827; − 1 GLO1; CODING 3 2952681; 2952683; (66.66%); UTR 2952684 (33.33%); Block_2442 0.009588 chr15: 42445498 . . . 42446391; − 1 PLA2G4F; CODING 2 3620449; 3620451 (100%); Block_2631 0.009588 chr15: 75108788 . . . 75123934; + 2 CPLX3; CODING 20 3601898; 3601899; LMAN1L; (55%); UTR 3601900; 3601902; (25%); 3601904; 3601906; INTRONIC 3601909; 3601911; (20%); 3601912; 3601919; 3601921; 3601914; 3601923; 3601926; 3601933; 3601934; 3601936; 3601937; 3601939; 3601940 Block_124 0.009728 chr1: 25573295 . . . 25573974; − 1 C1orf63; CODING 3 2402129; 2402130; (33.33%); UTR 2402134 (66.66%); Block_1788 0.009728 chr12: 123212329 . . . 123213804; − 1 GPR81; UTR (100%); 2 3475776; 3475778 Block_2163 0.009871 chr13: 111940732 . . . 111953191; + 1 ARHGEF7; CODING 2 3501737; 3501744 (100%); Block_4943 0.009871 chr3: 148896342 . . . 148897449; − 1 CP; CODING 2 2700265; 2700267 (100%); Block_7740 0.009871 chrX: 43571128 . . . 43605327; + 1 MAOA; CODING 14 4055670; 4055678; (92.85%); UTR 4055680; 3975248; (7.14%); 4055682; 3975250; 3975251; 4055686; 3975252; 3975253; 3975256; 3975258; 3975259; 3975260 Block_5168 0.010015 chr3: 156249230 . . . 156254535; + 1 KCNAB1; CODING 2 2649070; 2649077 (100%); Block_5185 0.010015 chr3: 175165052 . . . 175293963; + 1 NAALADL2; CODING 4 2653186; 2653187; (100%); 2653188; 2653192 Block_462 0.010161 chr1: 24840908 . . . 24867125; + 1 RCAN3; INTERGENIC 7 2325485; 2325490; (14.28%); 2325491; 2325494; CODING 2325497; 2325498; (57.14%); UTR 2325499 (28.57%); Block_3672 0.01061 chr19: 52462246 . . . 52469039; + 1 AC011460.1; INTRONIC 4 3839986; 3839988; (100%); 3839990; 3839992 Block_4273 0.01061 chr2: 220283450 . . . 220283756; + 1 DES; CODING 2 2528481; 2528482 (100%); Block_3289 0.010764 chr17: 66038430 . . . 66039426; + 1 KPNA2; CODING 5 3732630; 4041134; (100%); 3732632; 3732633; 4041130 Block_5774 0.010764 chr5: 132163477 . . . 132164924; − 1 SHROOM1; INTRONIC 2 2875520; 2875521 (100%); Block_2489 0.01092 chr15: 59428644 . . . 59450551; − 1 MYO1E; CODING 2 3626828; 3626837 (50%); UTR (50%); Block_6910 0.01092 chr8: 42033008 . . . 42050729; − 1 PLAT; CODING 13 3133235; 3133236; (84.61%); UTR 3133241; 3133242; (15.38%); 3133244; 3133248; 3133252; 3133254; 3133257; 3133259; 3133260; 3133263; 3133264 Block_5841 0.011077 chr5: 176981427 . . . 176981459; − 1 FAM193B; CODING 2 2888991; 2889081 (100%); Block_6141 0.011077 chr6: 38644052 . . . 38650635; − 1 GLO1; CODING 3 2952682; 2952685; (66.66%); UTR 2952686 (33.33%); Block_326 0.011237 chr1: 163112906 . . . 163122506; − 1 RGS5; CODING 7 2441391; 2441393; (42.85%); UTR 2441394; 2441395; (57.14%); 2441396; 2441398; 2441399 Block_1996 0.011399 chr13: 38137470 . . . 38138697; − 1 POSTN; CODING 2 3510070; 3510072 (100%); Block_2083 0.011399 chr13: 24157611 . . . 24190183; + 1 TNFRSF19; CODING 5 3481424; 3481425; (60%); 3481429; 3481433; ncTRANSCRIPT 3481434 (20%); INTRONIC (20%); Block_2847 0.011399 chr16: 28506463 . . . 28506488; + 1 APOBR; CODING 2 3686631; 3686648 (100%); Block_5302 0.011399 chr4: 80887049 . . . 80896290; − 1 ANTXR2; INTRONIC 2 2775010; 2775011 (100%); Block_607 0.011563 chr1: 110211967 . . . 110214138; + 1 GSTM2; CODING 4 2350963; 2350964; (100%); 2350971; 2350973 Block_6224 0.011563 chr6: 110932448 . . . 110991713; − 1 CDK19; CODING 10 2969474; 2969475; (80%); UTR 2969476; 2969479; (20%); 2969485; 2969488; 2969489; 2969493; 2969496; 2969499 Block_3268 0.011729 chr17: 57724893 . . . 57733355; + 1 CLTC; CODING 2 3729179; 3729186 (100%); Block_4504 0.011729 chr21: 29897038 . . . 29922984; − 1 AF131217.1; INTRONIC 2 3927867; 3927875 (100%); Block_1960 0.011897 chr12: 119631512 . . . 119632155; + 1 HSPB8; CODING 2 3434022; 3434023 (50%); UTR (50%); Block_228 0.011897 chr1: 94995124 . . . 95006762; − 1 F3; ncTRANSCRIPT 8 2423915; 2423916; (12.5%); 2423918; 2423920; CODING 2423923; 2423928; (62.5%); UTR 2423929; 2423930 (12.5%); INTRONIC (12.5%); Block_7829 0.011897 chrX: 135288595 . . . 135292180; + 1 FHL1; CODING 6 3992433; 3992434; (100%); 3992435; 3992439; 3992440; 3992448 Block_4343 0.012239 chr20: 20469819 . . . 20475748; − 1 RALGAPA2; INTRONIC 4 3900130; 3900133; (100%); 3900135; 3900136 Block_7167 0.012239 chr9: 5335054 . . . 5339746; − 1 RLN1; CODING 4 3197515; 3197516; (75%); UTR 3197518; 3197520 (25%); Block_2732 0.012414 chr16: 28123180 . . . 28123325; − 1 XPO6; CODING 2 3686351; 3686352 (100%); Block_4075 0.012414 chr2: 47604162 . . . 47606139; + 1 EPCAM; CODING 2 2480978; 2480980 (100%); Block_7007 0.012414 chr8: 144695086 . . . 144697077; − 1 TSTA3; CODING 6 3157663; 3157665; (100%); 3157670; 3157671; 3157674; 3157675 Block_969 0.012414 chr10: 100219332 . . . 100249939; − 1 HPSE2; CODING 2 3302888; 3302896 (100%); Block_3397 0.01259 chr18: 13574674 . . . 13585570; + 1 C18orf1; INTRONIC 2 3780272; 3780043 (100%); Block_1932 0.012769 chr12: 102011150 . . . 102079590; + 1 MYBPC1; CODING 36 3428611; 3428612; (69.44%); UTR 3428613; 3428617; (2.77%); 3428619; 3428620; INTRONIC 3428623; 3428624; (27.77%); 3428625; 3428626; 3428627; 3428628; 3428629; 3428630; 3428631; 3428634; 3428635; 3428636; 3428637; 3428638; 3428639; 3428640; 3428641; 3428642; 3428643; 3428644; 3428646; 3428647; 3428648; 3428650; 3428651; 3428654; 3428655; 3428659; 3428665; 3428666; Block_6157 0.012769 chr6: 49695711 . . . 49704193; − 1 CRISP3; CODING 8 2956567; 2956568; (87.5%); UTR 2956569; 2956571; (12.5%); 2956572; 2956573; 2956574; 2956575 Block_5365 0.01295 chr4: 121954556 . . . 121966964; − 1 C4orf31; CODING 3 2783896; 2783898; (33.33%); 2783906 INTERGENIC (33.33%); UTR (33.33%); Block_1650 0.013134 chr12: 44902385 . . . 44926477; − 1 NELL2; CODING 3 3451832; 3451841; (66.66%); UTR 3451843 (33.33%); Block_3093 0.013134 chr17: 74139170 . . . 74158083; − 1 RNF157; CODING 8 3771400; 3771403; (62.5%); UTR 3771404; 3771411; (37.5%); 3771416; 3771419; 3771421; 3771424 Block_5093 0.013134 chr3: 68057255 . . . 68057279; + 1 FAM19A1; INTRONIC 2 2628487; 4047275 (100%); Block_7271 0.013134 chr9: 114190325 . . . 114199375; − 1 KIAA0368; CODING 3 3220621; 3220627; (100%); 3220629 Block_243 0.013319 chr1: 110282086 . . . 110282515; − 1 GSTM3; CODING 2 2427224; 2427226 (100%); Block_5281 0.013507 chr4: 66465162 . . . 66468022; − 1 EPHA5; CODING 3 2771409; 2771411; (66.66%); 2771412 INTRONIC (33.33%); Block_931 0.013507 chr10: 81319697 . . . 81319724; − 1 SFTPA2; ncTRANSCRIPT 2 3297075; 3297138 (100%); Block_4894 0.013698 chr3: 123452947 . . . 123456357; − 1 MYLK; CODING 2 2692532; 2692536 (100%); Block_1775 0.013891 chr12: 118636857 . . . 118639157; − 1 TAOK3; CODING 2 3473836; 3473838 (100%); Block_2881 0.013891 chr16: 56692595 . . . 56693058; + 1 MT1F; CODING 2 3662206; 3662208 (100%); Block_5117 0.013891 chr3: 121603566 . . . 121604258; + 1 EAF2; INTRONIC 2 2638711; 2638712; (100%); Block_5827 0.013891 chr5: 176919406 . . . 176919436; − 1 PDLIM7; CODING 2 2888869; 2889110 (100%); Block_7423 0.013891 chr9: 90254565 . . . 90261474; + 1 DAPK1; CODING 7 3177926; 3177928; (100%); 3177929; 3177930; 3177932; 3177933; 3177934 Block_2772 0.014086 chr16: 66651699 . . . 66655784; − 1 CMTM4; UTR (100%); 4 3695162; 3695163; 3695166; 3695167 Block_3936 0.014086 chr2: 180306906 . . . 180409688; − 1 ZNF385B; ncTRANSCRIPT 13 2590020; 2590021; (7.69%); 2590022; 2590027; CODING 2590028; 2590029; (53.84%); UTR 2590033; 2590034; (23.07%); 2590038; 2590039; INTRONIC 2590129; 2590044; (15.38%); 2590045 Block_6264 0.014086 chr6: 136888801 . . . 136926464; − 1 MAP3K5; CODING 6 2975883; 2975891; (100%); 2975893; 2975896; 2975900; 2975901 Block_4719 0.014283 chr22: 48088744 . . . 48107002; + 1 RP11- INTRONIC 2 3949444; 3949447 191L9.4; (100%); Block_6353 0.014283 chr6: 31785240 . . . 31797461; + 2 HSPA1B; CODING 2 2902713; 2902730 HSPA1A; (100%); Block_6562 0.014283 chr7: 27234981 . . . 27237774; − 1 HOXA13; UTR (100%); 2 3042998; 3043001 Block_6873 0.014283 chr8: 19315164 . . . 19315317; − 1 CSGALN CODING 2 3126531; 3126532 ACT1; (50%); UTR (50%); Block_7134 0.014283 chr8: 104709474 . . . 104778764; + 1 RIMS2; CODING 3 3110435; 3110437; (100%); 3110438 Block_7639 0.014283 chrX: 106957605 . . . 106957732; − 1 TSC22D3; UTR (100%); 2 4017401; 4017402 Block_3675 0.014483 chr19: 53945049 . . . 53945553; + 1 CTD- ncTRANSCRIPT 2 3840864; 3840869 2224J9.2; (100%); Block_6206 0.014483 chr6: 94066465 . . . 94068123; − 1 EPHA7; CODING 2 2965235; 2965237 (100%); Block_2713 0.014686 chr16: 15797034 . . . 15950855; − 1 MYH11; CODING 43 3682029; 3682030; (97.67%); UTR 3682034; 3682035; (2.32%); 3682037; 3682041; 3682042; 3682043; 3682044; 3682045; 3682046; 3682047; 3682049; 3682050; 3682052; 3682054; 3682057; 3682062; 3682066; 3682067; 3682068; 3682071; 3682072; 3682076; 3682078; 3682079; 3682080; 3682082; 3682083; 3682084; 3682086; 3682091; 3682092; 3682094; 3682099; 3682103; 3682107; 3682109; 3682113; 3682118; 3682122; 3682129; 368213 Block_3833 0.014686 chr2: 100175340 . . . 100185376; − 1 AFF3; CODING 3 2566859; 2566862; (100%); 2566863 Block_5016 0.014686 chr3: 19295194 . . . 19322810; + 1 KCNH8; CODING 2 2613308; 2613316 (100%); Block_6327 0.014686 chr6: 16279026 . . . 16290811; + 1 GMPR; CODING 3 2896566; 2896570; (100%); 2896575 Block_2885 0.014891 chr16: 56972888 . . . 56975332; + 1 HERPUD1; INTRONIC 2 3662400; 3662405 (100%); Block_2888 0.014891 chr16: 57159781 . . . 57168720; + 1 CPNE2; CODING 2 3662570; 3662575 (100%); Block_1339 0.015098 chr11: 111779401 . . . 111782388; − 1 CRYAB; CODING 4 3391171; 3391173; (75%); UTR 3391176; 3391181 (25%); Block_6265 0.015308 chr6: 136934261 . . . 136944102; − 1 MAP3K5; CODING 2 2975904; 297590 (100%); Block_4088 0.015521 chr2: 61333740 . . . 61335484; + 1 KIAA1841; CODING 2 2484488; 2484489 (100%); Block_6205 0.015736 chr6: 93953170 . . . 93982106; − 1 EPHA7; CODING 9 2965209; 2965210; (100%); 2965211; 2965214; 2965218; 2965219; 2965222; 2965223; 2965224 Block_6418 0.015736 chr6: 76591424 . . . 76617955; + 1 MYO6; CODING 14 2914115; 2914118; (14.28%); 2914123; 2914124; INTRONIC 2914125; 2914126; (85.71%); 2914128; 2914130; 2914131; 2914134; 2914135; 2914136; 2914137; 2914139 Block_709 0.015736 chr1: 183079624 . . . 183111896; + 1 LAMC1; CODING 15 2371095; 2371102; (100%); 2371106; 2371107; 2371111; 2371115; 2371118; 2371120; 2371121; 2371122; 2371123; 2371124; 2371128; 2371132; 2371136 Block_5054 0.015953 chr3: 44926817 . . . 44955803; + 1 TGM4; ncTRANSCRIPT 24 2620356; 2620357; (4.16%); 2620358; 2620359; CODING 2620360; 2620361; (79.16%); UTR 2620362; 2620364; (8.33%); 2620366; 2620367; INTRONIC 2620368; 2620371; (8.33%); 2620373; 2620374; 2620375; 2620376; 2620381; 2620382; 2620384; 2620386; 2620387; 2620388; 2620389; 2620390 Block_2887 0.016174 chr16: 57155009 . . . 57155672; + 1 CPNE2; CODING 2 3662564; 3662565 (100%); Block_4137 0.016174 chr2: 111556187 . . . 111562970; + 1 ACOXL; CODING 3 2500189; 2500190; (100%); 2500193 Block_1980 0.016397 chr13: 24334264 . . . 24334353; − 1 MIPEP; CODING 2 3505466; 3505467 (100%); Block_6665 0.016851 chr7: 148701024 . . . 148716114; − 1 PDIA4; CODING 8 3078437; 3078440; (100%); 3078441; 3078445; 3078446; 3078447; 3078449; 3078453 Block_1982 0.017083 chr13: 24384023 . . . 24460604; − 1 MIPEP; CODING 13 3505485; 3505494; (100%); 3505495; 3505497; 3505499; 3505500; 3505504; 3505505; 3505506; 3505507; 3505508; 3505512; 3505517 Block_2679 0.017083 chr15: 101422111 . . . 101422244; + 1 ALDH1A3; INTRONIC 2 3611631; 3611632 (100%); Block_3008 0.017317 chr17: 38545810 . . . 38546338; − 1 TOP2A; CODING 2 3756196; 3756197 (100%); Block_5308 0.017317 chr4: 80957129 . . . 80976604; − 1 ANTXR2; CODING 3 2775032; 2775037; (100%); 2775038 Block_6709 0.017317 chr7: 27224759 . . . 27225870; + 1 HOXA11- ncTRANSCRIPT 5 2994152; 2994154; AS1; (100%); 2994156; 2994159; 2994160 Block_2215 0.017553 chr14: 51378696 . . . 51382203; − 1 PYGL; CODING 3 3564220; 3564225; (100%); 3564227 Block_6943 0.017553 chr8: 73978218 . . . 73982163; − 1 C8orf84; CODING 4 3140490; 3140491; (50%); UTR 3140492; 3140493 (50%); Block_2491 0.017793 chr15: 59480325 . . . 59497655; − 1 MYO1E; CODING 4 3626865; 3626867; (100%); 3626869; 3626871 Block_6591 0.017793 chr7: 80372319 . . . 80456803; − 1 SEMA3C; CODING 16 3058760; 3058761; (87.5%); UTR 3058762; 3058766; (12.5%); 3058768; 3058773; 3058778; 3058780; 3058784; 3058786; 3058787; 3058788; 3058789; 3058790; 3058794; 3058796 Block_7430 0.017793 chr9: 96026229 . . . 96031027; + 1 WNK2; CODING 2 3179747; 3179752 (100%); Block_1045 0.018281 chr10: 51555733 . . . 51556843; + 1 MSMB; CODING 2 3246411; 3246412 (100%); Block_2756 0.018281 chr16: 54953317 . . . 54954239; − 1 CRNDE; ncTRANSCRIPT 2 3692520; 3692521 (50%); INTRONIC (50%); Block_363 0.018281 chr1: 203310039 . . . 203317324; − 1 FMOD; CODING 10 2451698; 2451699; (40%); UTR 2451700; 2451701; (60%); 2451702; 2451703; 2451704; 2451710; 2451711; 2451712 Block_370 0.018281 chr1: 205627208 . . . 205634013; − 1 SLC45A3; CODING 8 2452616; 2452617; (75%); UTR 2452618; 2452619; (25%); 2452621; 2452622; 2452623; 2452624 Block_6688 0.018281 chr7: 2565880 . . . 2566535; + 1 LFNG; CODING 2 2987566; 2987568 (100%); Block_7188 0.018281 chr9: 35682105 . . . 35689177; − 1 TPM2; CODING 6 3204723; 3204730; (100%); 3204734; 3204737; 3204739; 3204740 Block_1179 0.018529 chr11: 2016621 . . . 2017401; − 1 H19; ncTRANSCRIPT 4 3359080; 3359084; (100%); 3359085; 3359087 Block_7359 0.018529 chr9: 140375422 . . . 140389574; − 1 PNPLA7; CODING 3 3231051; 3231059; (100%); 3231063 Block_1193 0.018781 chr11: 6653316 . . . 6661474; − 1 DCHS1; CODING 2 3361093; 3361099 (100%); Block_1474 0.018781 chr11: 65194527 . . . 65211475; + 1 NEAT1; ncTRANSCRIPT 8 3335225; 3335227; (100%); 3335229; 3335231; 3335233; 3335235; 3335239; 3335240 Block_2362 0.018781 chr14: 68113486 . . . 68115462; + 1 ARG2; INTRONIC 2 3541413; 3541416 (100%); Block_2983 0.019035 chr17: 26958501 . . . 26966660; − 1 KIAA0100; CODING 5 3750898; 3750901; (100%); 3750909; 3750911; 3750917 Block_6098 0.019035 chr6: 24666778 . . . 24666965; − 1 TDP2; CODING 2 2945667; 2945670 (100%); Block_6856 0.019035 chr7: 155100327 . . . 155101637; + 1 INSIG1; UTR (100%); 2 3033258; 3033259 Block_1040 0.019292 chr10: 43615579 . . . 43622087; + 1 RET; CODING 3 3243877; 3243878; (100%); 3243881 Block_1047 0.019292 chr10: 51562272 . . . 51562497; + 1 MSMB; CODING 2 3246417; 3246418 (50%); UTR (50%); Block_4023 0.019292 chr2: 11724711 . . . 11731961; + 1 GREB1; UTR (50%); 2 2469853; 2469863 INTRONIC (50%); Block_1044 0.019553 chr10: 51532298 . . . 51535286; + 2 TIMM23B; ncTRANSCRIPT 4 3246373; 3246408; RP11- (50%); 3246374; 3246376 481A12.2; INTRONIC (50%); Block_2252 0.019553 chr14: 76446944 . . . 76447361; − 1 TGFB3; CODING 2 3572536; 3572538 (50%); UTR (50%); Block_2547 0.019553 chr15: 90328249 . . . 90349999; − 1 ANPEP; CODING 25 3638608; 3638609; (92%); UTR 3638610; 3638611; (8%); 3638612; 3638614; 3638615; 3638616; 3638622; 3638623; 3638624; 3638625; 3638631; 3638633; 3638635; 3638637; 3638639; 3638640; 3638641; 3638643; 3638644; 3638645; 3638646; 3638648; 3638649 Block_4065 0.019553 chr2: 39944177 . . . 39944970; + 1 TMEM178; CODING 3 2478298; 2478299; (33.33%); UTR 2478300 (66.66%); Block_4351 0.019553 chr20: 20634174 . . . 20661443; − 1 RALGAPA2; CODING 2 3900240; 3900249 (100%); Block_5767 0.019553 chr5: 121405764 . . . 121406282; − 1 LOX; CODING 3 2872855; 2872856; (100%); 2872857 Block_1076 0.019816 chr10: 77453352 . . . 77454380; + 1 C10orf11; INTRONIC 2 3252742; 3252954 (100%); Block_3269 0.020083 chr17: 57741220 . . . 57763148; + 1 CLTC; CODING 16 3729191; 3729193; (100%); 3729194; 3729195; 3729196; 3729199; 3729201; 3729202; 3729206; 3729207; 3729208; 3729209; 3729210; 3729213; 3729216; 3729218 Block_3886 0.020083 chr2: 162883071 . . . 162891670; − 1 DPP4; UTR (33.33%); 3 2584060; 2584063; INTRONIC 2584065 (66.66%); Block_5453 0.020083 chr4: 15839733 . . . 15852471; + 1 CD38; INTERGENIC 5 2719689; 2719692; (20%); 2719694; 2719695; CODING 2719696 (60%); UTR (20%); Block_1965 0.020352 chr12: 121138015 . . . 121138614; + 1 MLEC; UTR (100%); 2 3434547; 3434548 Block_3245 0.020352 chr17: 45753775 . . . 45754478; + 1 KPNB1; CODING 2 3724808; 3724810 (100%); Block_6543 0.020352 chr7: 6502772 . . . 6505843; − 1 KDELR2; CODING 2 3037394; 3037396 (100%); Block_7361 0.020625 chr9: 140437902 . . . 140444736; − 1 PNPLA7; CODING 4 3231109; 3231112; (75%); UTR 3231115; 3231117 (25%); Block_7520 0.020625 chrX: 229408 . . . 229432; − 1 GTPBP6; ncTRANSCRIPT 2 3997098; 4032902 (100%); Block_1934 0.020901 chr12: 104335273 . . . 104336343; + 1 HSP90B1; CODING 4 3429327; 3429329; (100%); 3429330; 3429331 Block_6150 0.020901 chr6: 46846004 . . . 46851982; − 1 GPR116; CODING 5 2955898; 2955900; (100%); 2955904; 2955908; 2955911 Block_6388 0.020901 chr6: 44752539 . . . 44800262; + 1 SUPT3H; INTRONIC_AS 3 2908668; 2908682; (33.33%); 2908684 INTERGENIC (33.33%); CODING_AS (33.33%); Block_1716 0.02118 chr12: 81655761 . . . 81661862; − 1 PPFIA2; CODING 2 3463825; 3463833 (100%); Block_1734 0.02118 chr12: 103238114 . . . 103246723; − 1 PAH; CODING 3 3468493; 3468497; (100%); 3468501 Block_330 0.02118 chr1: 169434441 . . . 169446972; − 1 SLC19A2; CODING 7 2443338; 2443339; (85.71%); UTR 2443342; 2443344; (14.28%); 2443345; 2443351; 2443352 Block_1239 0.021462 chr11: 49175403 . . . 49229959; − 1 FOLH1; CODING 3 3372906; 3372936; (100%); 3372937 Block_2310 0.021462 chr14: 38033662 . . . 38058763; + 0 INTERGENIC 4 3533021; 3533028; (100%); 3533041; 3533045 Block_3594 0.021748 chr19: 15729440 . . . 15730475; + 1 CYP4F8; CODING 2 3823269; 3823272 (50%); INTRONIC (50%); Block_6355 0.021748 chr6: 31901946 . . . 31903811; + 1 C2; CODING 2 2902816; 2902819 (100%); Block_6731 0.021748 chr7: 56130382 . . . 56131617; + 1 CCT6A; CODING 3 3003220; 3003225; (33.33%); UTR 3003226 (66.66%); Block_2492 0.022037 chr15: 59506427 . . . 59506888; − 1 MYO1E; CODING 2 3626878; 3626879 (100%); Block_4981 0.022037 chr3: 189787406 . . . 189823386; − 1 LEPREL1; INTRONIC 2 2710531; 2710536 (100%); Block_6399 0.022037 chr6: 57311563 . . . 57324709; + 1 PRIM2; INTRONIC 2 2911450; 2911483 (100%); Block_7292 0.022037 chr9: 128000931 . . . 128003092; − 1 HSPA5; CODING 4 3225407; 3225408; (100%); 3225411; 3225416 Block_3621 0.022329 chr19: 35611982 . . . 35613858; + 1 FXYD3; CODING 3 3830179; 3830181; (100%); 3830183 Block_7270 0.022329 chr9: 114176751 . . . 114182394; − 1 KIAA0368; CODING 4 3220599; 3220601; (100%); 3220603; 3220609 Block_4024 0.022625 chr2: 13749190 . . . 13929969; + 3 NCRNA00276; INTERGENIC 15 2470320; 2470321; AC016730.1; (53.33%); 2470322; 2470323; AC092635.1; ncTRANSCRIPT 2470324; 2470325; (20%); 2470328; 2470330; INTRONIC 2470331; 2470333; (6.66%); 2470334; 2470335; INTRONIC_AS 2470344; 2470346; (20%); 2470354 Block_5388 0.022625 chr4: 143326360 . . . 143383879; − 1 INPP4B; CODING 6 2787554; 2787555; (66.66%); UTR 2787562; 2787563; (33.33%); 2787564; 2787567 Block_6426 0.022625 chr6: 88210238 . . . 88218297; + 1 SLC35A1; CODING 5 2916360; 2916361; (100%); 2916363; 2916365; 2916372 Block_2538 0.022924 chr15: 76254177 . . . 76301622; − 1 NRG4; CODING 3 3633708; 3633710; (66.66%); UTR 3633715 (33.33%); Block_7429 0.022924 chr9: 95993221 . . . 96000589; + 1 WNK2; CODING 3 3179723; 3179725; (100%); 3179726 Block_1325 0.023226 chr11: 102269452 . . . 102272423; − 1 TMEM123; CODING 2 3388634; 3388639 (50%); UTR (50%); Block_2166 0.023226 chr13: 113751561 . . . 113752679; + 2 MCF2L; CODING 2 3502390; 3502391 AL137002.1; (50%); UTR (50%); Block_2361 0.023226 chr14: 68086731 . . . 68118330; + 1 ARG2; CODING 8 3541396; 3541398; (87.5%); UTR 3541407; 3541412; (12.5%); 3541414; 3541415; 3541420; 3541421 Block_2982 0.023226 chr17: 26948047 . . . 26962543; − 1 KIAA0100; CODING 6 3750892; 3750900; (100%); 3750904; 3750905; 3750907; 3750910 Block_4388 0.023226 chr20: 48122492 . . . 48160955; − 1 PTGIS; CODING 5 3908938; 3908939; (60%); 3908943; 3908951; INTERGENIC 3908952 (20%); UTR (20%); Block_4862 0.023226 chr3: 115561318 . . . 115571410; − 1 LSAMP; CODING 2 2690039; 2690041 (100%); Block_4905 0.023226 chr3: 129123093 . . . 129137223; − 1 C3orf25; CODING 2 2694763; 2694771 (100%); Block_1408 0.023532 chr11: 17304338 . . . 17352512; + 1 NUCB2; CODING 12 3322265; 3322271; (91.66%); UTR 3322272; 3322276; (8.33%); 3322277; 3322278; 3322279; 3322280; 3322281; 3322283; 3322287; 3322289 Block_2086 0.023532 chr13: 24289383 . . . 24309286; + 1 MIPEP; INTERGENIC 11 3481477; 3481478; (72.72%); 3481487; 3481489; ncTRANSCRIPT_AS 3481491; 3481493; (18.18%); 3481479; 3481475; INTRONIC_AS 3481480; 3481481; (9.09%); 3481495 Block_2821 0.023532 chr16: 8839879 . . . 8862784; + 1 ABAT; CODING 6 3647456; 3647459; (100%); 3647462; 3647467; 3647468; 3647472 Block_5471 0.023532 chr4: 41395354 . . . 41395449; + 1 LIMCH1; INTRONIC 2 2725082; 2725083 (100%); Block_745 0.023532 chr1: 203311379 . . . 203316520; + 1 FMOD; ncTRANSCRIPT_AS 2 2375681; 2375682 (50%); INTRONIC_AS (50%); Block_2886 0.023842 chr16: 56975974 . . . 56977926; + 1 HERPUD1; INTERGENIC 2 3662406; 3662413 (50%); INTRONIC (50%); Block_4945 0.023842 chr3: 149086852 . . . 149095329; − 1 TM4SF1; CODING 5 2700368; 2700372; (80%); UTR 2700374; 2700376; (20%); 2700379 Block_6419 0.023842 chr6: 76604531 . . . 76626280; + 1 MYO6; CODING 8 2914127; 2914129; (62.5%); UTR 2914138; 2914140; (37.5%); 2914146; 2914147; 2914148; 2914149 Block_6154 0.024155 chr6: 47251674 . . . 47252155; − 1 TNFRSF21; CODING 2 2956076; 2956077 (100%); Block_1388 0.024471 chr11: 4730763 . . . 4740320; + 2 AC103710.1; CODING 4 3318188; 3318189; MMP26; (25%); 3318226; 3318229 INTRONIC (75%); Block_2898 0.024471 chr16: 67203603 . . . 67203747; + 1 HSF4; CODING 2 3665259; 3665260 (100%); Block_4522 0.024791 chr21: 39858595 . . . 39862882; − 1 ERG; INTRONIC 2 3931864; 3931914 (100%); Block_5306 0.024791 chr4: 80918912 . . . 80949988; − 1 ANTXR2; INTRONIC 3 2775059; 2775027; (100%); 2775028 Block_2184 0.025443 chr14: 23816393 . . . 23816935; − 1 SLC22A17; CODING 2 3557354; 3557358 (100%); Block_2254 0.025443 chr14: 80666635 . . . 80668673; − 1 DIO2; UTR (100%); 2 3573882; 3573883 Block_2435 0.025443 chr15: 37217501 . . . 37225462; − 1 MEIS2; INTRONIC 2 3618372; 3618379 (100%); Block_2648 0.025774 chr15: 86212981 . . . 86228071; + 1 AKAP13; CODING 3 3606399; 3606405; (100%); 3606409 Block_3540 0.025774 chr19: 51410040 . . . 51412584; − 1 KLK4; CODING 7 3868736; 3868737; (85.71%); UTR 3868738; 3868740; (14.28%); 3868741; 3868743; 3868745 Block_3894 0.025774 chr2: 166737190 . . . 166758405; − 1 TTC21B; CODING 4 2585261; 2585265; (100%); 2585273; 2585274 Block_4572 0.025774 chr21: 42648718 . . . 42652968; + 0 INTERGENIC 2 3921988; 3921989 (100%); Block_1981 0.026108 chr13: 24348459 . . . 24352051; − 1 MIPEP; INTRONIC 3 3505475; 3505477; (100%); 3505478 Block_2146 0.026108 chr13: 99099031 . . . 99100596; + 1 FARP1; CODING 2 3498038; 3498041 (50%); UTR (50%); Block_5418 0.026108 chr4: 170137651 . . . 170167646; − 1 SH3RF1; INTRONIC 2 2793179; 2793181 (100%); Block_1963 0.026447 chr12: 121132919 . . . 121134161; + 1 MLEC; CODING 2 3434539; 3434541 (100%); Block_6398 0.026447 chr6: 57270903 . . . 57311752; + 1 PRIM2; ncTRANSCRIPT 7 2911447; 2911470; (14.28%); 2911448; 2911473; INTRONIC 2911475; 2911451; (85.71%); 2911452 Block_1159 0.026789 chr10: 125726574 . . . 125726620; + 0 INTERGENIC 2 3311091; 4038113 (100%); Block_182 0.026789 chr1: 59246516 . . . 59249254; − 1 JUN; CODING 9 2415086; 2415088; (33.33%); UTR 2415090; 2415091; (66.66%); 2415093; 2415094; 2415096; 2415098; 2415099 Block_2594 0.026789 chr15: 57745886 . . . 57754067; + 1 CGNL1; CODING 2 3595336; 3595342 (100%); Block_2880 0.026789 chr16: 56667710 . . . 56678081; + 4 MT1JP; ncTRANSCRIPT 5 3662156; 3662163; MT1DP; (20%); 3662122; 3662124; MT1M; CODING 3662175 MT1A; (80%); Block_3661 0.026789 chr19: 49699887 . . . 49703683; + 1 TRPM4; CODING 2 3838347; 3838348 (100%); Block_5184 0.026789 chr3: 174951778 . . . 174974294; + 1 NAALADL2; CODING 3 2653162; 2653163; (100%); 2653164 Block_241 0.027135 chr1: 110276731 . . . 110279596; − 1 GSTM3; UTR (100%); 2 2427209; 2427213 Block_2441 0.027135 chr15: 42437997 . . . 42439930; − 1 PLA2G4F; CODING 3 3620436; 3620439; (100%); 3620441 Block_3238 0.027135 chr17: 44828869 . . . 44832729; + 1 NSF; CODING 2 3724262; 3724264 (100%); Block_6472 0.027135 chr6: 144904413 . . . 144904734; + 1 UTRN; CODING 2 2929285; 2929286 (50%); UTR (50%); Block_6883 0.027135 chr8: 26611808 . . . 26614843; − 1 ADRA1A; CODING 2 3128825; 3128829 (50%); INTRONIC (50%); Block_7532 0.027135 chrX: 1505179 . . . 1505423; − 1 SLC25A6; UTR (100%); 2 3997377; 4033178 Block_1298 0.027485 chr11: 72468829 . . . 72470411; − 1 STARD10; CODING 2 3381326; 3381331 (100%); Block_3532 0.027839 chr19: 46280628 . . . 46281019; − 1 DMPK; CODING 2 3865653; 3865654 (100%); Block_6942 0.027839 chr8: 72211297 . . . 72246402; − 1 EYA1; CODING 6 3140094; 3140095; (100%); 3140101; 3140103; 3140106; 3140109 Block_7269 0.027839 chr9: 114151836 . . . 114170935; − 1 KIAA0368; CODING 4 3220569; 3220571; (100%); 3220577; 3220589 Block_751 0.027839 chr1: 207497909 . . . 207504583; + 1 CD55; CODING 3 2377239; 2377242; (100%); 2377245 Block_1050 0.028197 chr10: 60559972 . . . 60573731; + 1 BICC1; CODING 2 3247880; 3247887 (100%); Block_7720 0.028197 chrX: 18597972 . . . 18606218; + 1 CDKL5; CODING 2 3970672; 3970676 (100%); Block_7402 0.028925 chr9: 71080046 . . . 71114251; + 1 PGM5; CODING 4 3173537; 3173540; (100%); 3173541; 3173543 Block_1619 0.029294 chr12: 16703175 . . . 16713472; − 1 LMO3; CODING 3 3446141; 3446142; (66.66%); UTR 3446145 (33.33%); Block_4177 0.029294 chr2: 160082200 . . . 160087326; + 1 TANC1; CODING 2 2512182; 2512191 (100%); Block_4425 0.029294 chr20: 21312923 . . . 21329067; + 1 XRN2; CODING 4 3879487; 3879492; (100%); 3879498; 3879506 Block_7830 0.029294 chrX: 135289915 . . . 135291372; + 1 FHL1; INTRONIC 2 3992437; 3992441 (100%); Block_1575 0.029668 chr11: 134130954 . . . 134131239; + 1 ACAD8; CODING 2 3357326; 3357327 (100%); Block_2381 0.030046 chr14: 88553185 . . . 88560834; + 0 INTERGENIC 2 3547415; 3547424 (100%); Block_5332 0.030046 chr4: 89199385 . . . 89199620; − 1 PPM1K; CODING 2 2777363; 2777364; (100%); Block_6163 0.030046 chr6: 55618961 . . . 55620476; − 1 BMP5; CODING 2 2958174; 2958176 (50%); UTR (50%); Block_6705 0.030046 chr7: 23286477 . . . 23314622; + 1 GPNMB; CODING 10 2992816; 2992825; (90%); UTR 2992827; 2992831; (10%); 2992832; 2992840; 2992842; 2992845; 2992847; 2992848 Block_6774 0.030046 chr7: 99159637 . . . 99167388; + 1 ZNF655; CODING 5 3014911; 3014912; (20%); 3014913; 3014917; ncTRANSCRIPT 3014954 (20%); UTR (20%); INTRONIC (40%); Block_7531 0.030046 chrX: 1505060 . . . 1505127; − 1 SLC25A6; UTR (100%); 2 3997376; 4033177 Block_1730 0.030428 chr12: 102153818 . . . 102164296; − 1 GNPTAB; CODING 9 3468120; 3468121; (100%); 3468122; 3468123; 3468126; 3468131; 3468134; 3468135; 3468136 Block_4021 0.030428 chr2: 11680067 . . . 11782662; + 1 GREB1; CODING 33 2469828; 2469836; (87.87%); UTR 2469837; 2469841; (12.12%); 2469849; 2469857; 2469861; 2469865; 2469866; 2469867; 2469868; 2469869; 2469870; 2469874; 2469876; 2469877; 2469880; 2469881; 2469882; 2469884; 2469887; 2469889; 2469891; 2469892; 2469893; 2469894; 2469896; 2469897; 2469898; 2469899; 2469900; 2469901; 2469902 Block_4231 0.030428 chr2: 198948634 . . . 198950883; + 1 PLCL1; CODING 2 2521607; 2521608 (100%); Block_265 0.030814 chr1: 144892521 . . . 144892549; − 1 PDE4DIP; CODING 2 2431960; 4042079 (100%); Block_6777 0.030814 chr7: 99169519 . . . 99170579; + 1 ZNF655; CODING 2 3014924; 3014928 (50%); INTRONIC (50%); Block_2757 0.031205 chr16: 55844435 . . . 55855323; − 1 CES1; CODING 6 3692709; 3661846; (100%); 3692711; 3661834; 3661831; 3692722 Block_4573 0.031205 chr21: 42694866 . . . 42729633; + 1 FAM3B; CODING 8 3922003; 3922012; (87.5%); UTR 3922017; 3922023; (12.5%); 3922027; 3922028; 3922031; 3922032 Block_6941 0.031205 chr8: 72156865 . . . 72182058; − 1 EYA1; CODING 2 3140079; 3140083 (100%); Block_3246 0.0316 chr17: 45755412 . . . 45755765; + 1 KPNB1; CODING 2 3724811; 3724812 (100%); Block_5372 0.0316 chr4: 138451013 . . . 138453177; − 1 PCDH18; CODING 4 4047508; 2786238; (100%); 2786239; 4047511 Block_710 0.0316 chr1: 183077411 . . . 183087270; + 1 LAMC1; CODING 3 2371094; 2371103; (100%); 2371108 Block_1362 0.031998 chr11: 122929505 . . . 122930647; − 1 HSPA8; CODING 4 3395428; 3395433; (100%); 3395438; 3395439 Block_139 0.031998 chr1: 38041207 . . . 38042091; − 1 GNL2; CODING 2 2407202; 2407204 (100%); Block_242 0.031998 chr1: 110280148 . . . 110280790; − 1 GSTM3; CODING 2 2427219; 2427222 (100%); Block_2615 0.031998 chr15: 69855990 . . . 69863685; + 1 AC100826.1; ncTRANSCRIPT 5 3599886; 3599887; (80%); 3599888; 3599890; INTRONIC 3599891 (20%); Block_6882 0.031998 chr8: 23540117 . . . 23540330; − 1 NKX3-1; CODING 3 3127991; 3127992; (100%); 3127994 Block_1049 0.032402 chr10: 60553245 . . . 60556259; + 1 BICC1; CODING 2 3247875; 3247877 (100%); Block_2731 0.032402 chr16: 28109882 . . . 28137158; − 1 XPO6; CODING 8 3686341; 3686343; (100%); 3686347; 3686348; 3686349; 3686353; 3686356; 3686361 Block_7314 0.032402 chr9: 136230241 . . . 136230349; − 1 SURF4; CODING 2 3228678; 4051970 (100%); Block_3435 0.032809 chr18: 56054957 . . . 56057598; + 1 NEDD4L; INTRONIC 7 3790090; 3790091; (100%); 3790092; 3790094; 3790095; 3790097; 3790098 Block_4064 0.032809 chr2: 39931241 . . . 39931334; + 1 TMEM178; CODING 2 2478287; 2478288 (100%); Block_4333 0.032809 chr20: 6090960 . . . 6096685; − 1 FERMT1; CODING 2 3896652; 3896654 (100%); Block_1256 0.033221 chr11: 62303454 . . . 62304039; − 1 AHNAK; CODING 2 3375784; 3375785 (50%); UTR (50%); Block_5294 0.033221 chr4: 76846890 . . . 76861308; − 1 NAAA; CODING 2 2773891; 2773897 (100%); Block_6360 0.033638 chr6: 32868955 . . . 32870947; + 1 AL669918.1; ncTRANSCRIPT 2 2903325; 2903327 (100%); Block_6542 0.033638 chr7: 6210524 . . . 6210945; − 1 CYTH3; CODING 2 3037270; 3037272 (100%); Block_1389 0.034059 chr11: 4788501 . . . 5009539; + 6 OR51F2; CODING 13 3318193; 3318195; OR51A8P; (46.15%); 3318240; 3318241; OR51H2P; ncTRANSCRIPT 3318242; 3318200; OR51T1; (38.46%); 3318205; 3318206; MMP26; UTR (7.69%); 3318210; 3318211; OR51N1P; INTRONIC 3318246; 3318247; (7.69%); 3318215 Block_876 0.034059 chr10: 43881590 . . . 43882061; − 1 HNRNPF; UTR (100%); 2 3286289; 3286290 Block_2883 0.034484 chr16: 56968915 . . . 56970561; + 1 HERPUD1; INTRONIC 3 3662392; 3662394; (100%); 3662396 Block_2971 0.034484 chr17: 17398026 . . . 17399476; − 1 RASD1; CODING 5 3747795; 3747796; (80%); UTR 3747797; 3747799; (20%); 3747801 Block_5102 0.034484 chr3: 105243191 . . . 105266352; + 1 ALCAM; CODING 5 2634545; 2634550; (100%); 2634552; 2634561; 2634562 Block_5301 0.034484 chr4: 80825530 . . . 80828621; − 1 ANTXR2; CODING 6 2774995; 2774996; (16.66%); UTR 2774997; 2774999; (83.33%); 2775000; 2775001 Block_5537 0.034484 chr4: 89588558 . . . 89602441; + 1 HERC3; CODING 3 2735499; 2735503; (100%); 2735510 Block_3902 0.035348 chr2: 168986056 . . . 168997267; − 1 STK39; CODING 2 2585761; 2585766 (100%); Block_1515 0.035787 chr11: 108010817 . . . 108017045; + 1 ACAT1; CODING 2 3347636; 3347644 (100%); Block_158 0.035787 chr1: 51768040 . . . 51768245; − 1 TTC39A; CODING 2 2412328; 2412330 (100%); Block_168 0.035787 chr1: 53363109 . . . 53370744; − 1 ECHDC2; CODING 3 2413037; 2413040; (100%); 2413044 Block_3529 0.035787 chr19: 45016075 . . . 45029277; − 1 CEACAM20; ncTRANSCRIPT 8 3864953; 3864956; (100%); 3864957; 3864959; 3864961; 3864962; 3864964; 3864967 Block_6470 0.035787 chr6: 144835069 . . . 144872213; + 1 UTRN; CODING 5 2929254; 2929260; (100%); 2929262; 2929268; 2929274 Block_7040 0.035787 chr8: 26265556 . . . 26265860; + 1 BNIP3L; CODING 2 3091030; 3091031 (100%); Block_2735 0.036231 chr16: 28493570 . . . 28493624; − 1 CLN3; INTRONIC 2 3654751; 3654816 (100%); Block_6533 0.036231 chr6: 168351907 . . . 168352865; + 1 MLLT4; CODING 2 2936935; 2936937 (100%); Block_881 0.036231 chr10: 46969414 . . . 46969439; − 1 SYT15; CODING 2 3287392; 4038216 (100%); Block_5436 0.036679 chr4: 187516851 . . . 187557363; − 1 FAT1; CODING 17 2797405; 2797407; (100%); 2797408; 2797410; 2797411; 2797414; 2797415; 2797418; 2797423; 2797426; 2797427; 2797430; 2797433; 2797435; 2797437; 2797438; 2797446 Block_5623 0.036679 chr4: 165691596 . . . 165722585; + 1 RP11- ncTRANSCRIPT 2 2750414; 2750417 294O2.2; (100%); Block_3420 0.037132 chr18: 48581190 . . . 48586286; + 1 SMAD4; CODING 2 3788324; 3788330 (100%); Block_3937 0.037132 chr2: 181436457 . . . 181469005; − 1 AC009478.1; ncTRANSCRIPT 2 2590308; 2590313 (50%); INTRONIC (50%); Block_5634 0.037132 chr4: 174109607 . . . 174135233; + 1 GALNT7; INTRONIC 2 2751944; 2751947 (100%); Block_5681 0.03759 chr5: 40760621 . . . 40767760; − 1 AC008810.1; CODING 4 2854740; 2854741; (50%); UTR 2854743; 2854749 (50%); Block_6148 0.03759 chr6: 46669622 . . . 46690628; − 2 TDRD6; CODING 13 2955823; 2955825; PLA2G7; (76.92%); 2955826; 2955830; UTR_AS 2955835; 2955836; (15.38%); 2955837; 2955838; CODING_AS 2955839; 2955840; (7.69%); 2955841; 2955842; 2955844 Block_6211 0.03759 chr6: 99853979 . . . 99857124; − 1 SFRS18; CODING 2 2966275; 2966279 (100%); Block_1312 0.038053 chr11: 85445044 . . . 85469138; − 1 SYTL2; CODING 6 3385111; 3385113; (83.33%); UTR 3385114; 3385117; (16.66%); 3385121; 3385123 Block_2926 0.038053 chr16: 84495374 . . . 84497337; + 1 ATP2C2; CODING 2 3671793; 3671798 (100%); Block_4520 0.038053 chr21: 39752360 . . . 39852761; − 1 ERG; ncTRANSCRIPT 65 3931784; 3931785; (4.61%); 3931786; 3931787; CODING 3931788; 3931789; (20%); UTR 3931790; 3931791; (13.84%); 3931792; 3931793; INTRONIC 3931794; 3931796; (61.53%); 3931798; 3931799; 3931800; 3931801; 3931802; 3931803; 3931804; 3931806; 3931807; 3931808; 3931809; 3931810; 3931811; 3931813; 3931814; 3931815; 3931816; 3931817; 3931818; 3931819; 3931820; 3931821; 3931822; 3931824; 3931827; 3931828; 3931829; 3931830; 3931831; 3931832; 3931833; 3931835; 3931836; 3931837; 3931838; 3931840; 3931841; 3931843; 3931844; 3931845; 3931846; 3931848; 3931849; 3931851; 3931852; 3931853; 3931854; 3931856; 3931857; 3931858; 3931859; 3931861; 3931862 Block_6855 0.038053 chr7: 155093280 . . . 155100014; + 1 INSIG1; CODING 4 3033244; 3033247; (100%); 3033249; 3033256 Block_7161 0.038053 chr9: 3223306 . . . 3228889; − 1 RFX3; CODING 2 3196843; 3196849 (50%); UTR (50%); Block_2625 0.03852 chr15: 73028188 . . . 73029911; + 1 BBS4; CODING 3 3600996; 3600997; (100%); 3600999 Block_7163 0.03852 chr9: 3277354 . . . 3301613; − 1 RFX3; CODING 4 3196877; 3196878; (100%); 3196879; 3196881 Block_34 0.038993 chr1: 2336552 . . . 2337237; − 1 PEX10; CODING 2 2392426; 2392427 (50%); UTR (50%); Block_4718 0.038993 chr22: 48031017 . . . 48082931; + 1 RP11- ncTRANSCRIPT 3 3949433; 3949438; 191L9.4; (100%); 3949440 Block_6477 0.038993 chr6: 145142024 . . . 145157563; + 1 UTRN; CODING 3 2929340; 2929344; (100%); 2929351 Block_3502 0.03947 chr19: 13050901 . . . 13051160; − 1 CALR; CODING_AS 2 3851902; 3851903 (100%); Block_5414 0.03947 chr4: 169919358 . . . 169928001; − 1 CBR4; CODING 3 2793091; 2793093; (33.33%); 2793098 INTRONIC (66.66%); Block_7223 0.03947 chr9: 93983092 . . . 93983273; − 1 AUH; CODING 2 3214385; 3214386 (100%); Block_2127 0.039952 chr13: 76374862 . . . 76378658; + 1 LMO7; CODING 2 3494192; 3494194 (100%); Block_3580 0.039952 chr19: 11210844 . . . 11213743; + 1 LDLR; INTRONIC 2 3821020; 3821024 (100%); Block_4531 0.039952 chr21: 42839814 . . . 42841274; − 1 TMPRSS2; ncTRANSCRIPT 3 3933046; 3933048; (66.66%); 3933049 INTRONIC (33.33%); Block_5329 0.039952 chr4: 88261689 . . . 88293951; − 1 HSD17B11; CODING 2 2777078; 2777086 (100%); Block_5367 0.039952 chr4: 122590800 . . . 122592788; − 1 ANXA5; CODING 2 2784046; 2784049 (100%); Block_1456 0.040439 chr11: 58385590 . . . 58387157; + 1 ZFP91; UTR (100%); 2 3331770; 3331771 Block_376 0.040439 chr1: 216824320 . . . 216850671; − 1 ESRRG; CODING 2 2455970; 2455975 (100%); Block_7353 0.040439 chr9: 140356003 . . . 140357262; − 1 PNPLA7; CODING 6 3231020; 4051802; (100%); 3231024; 4051804; 3231029; 4051807 Block_1476 0.040932 chr11: 65273777 . . . 65273907; + 1 MALAT1; ncTRANSCRIPT 2 3335195; 3335196 (100%); Block_5017 0.040932 chr3: 19389238 . . . 19498406; + 1 KCNH8; CODING 6 2613328; 2613336; (100%); 2613337; 2613340; 2613342; 2613344 Block_6874 0.040932 chr8: 19325762 . . . 19339547; − 1 CSGALNACT1; INTRONIC 4 3126537; 3126539; (100%); 3126540; 3126543 Block_258 0.041429 chr1: 120295908 . . . 120307209; − 1 HMGCS2; CODING 9 2431038; 2431042; (100%); 2431044; 2431047; 2431050; 2431051; 2431056; 2431057; 2431058 Block_3899 0.041429 chr2: 168825060 . . . 168864496; − 1 STK39; INTRONIC 2 2585709; 2585717 (100%); Block_4051 0.041429 chr2: 30748528 . . . 30785140; + 1 LCLAT1; CODING 2 2475742; 2475748 (100%); Block_5419 0.041429 chr4: 170190133 . . . 170190434; − 1 SH3RF1; CODING 2 2793189; 2793190 (50%); UTR (50%); Block_6180 0.041429 chr6: 75822940 . . . 75902036; − 1 COL12A1; CODING 40 2961207; 2961209; (100%); 2961210; 2961211; 2961218; 2961222; 2961224; 2961225; 2961227; 2961229; 2961230; 2961231; 2961232; 2961233; 2961234; 2961237; 2961239; 2961240; 2961242; 2961244; 2961247; 2961248; 2961251; 2961252; 2961253; 2961254; 2961256; 2961257; 2961258; 2961259; 2961260; 2961261; 2961263; 2961264; 2961266; 2961267; 2961268; 2961270; 2961271; 2961273 Block_7008 0.041429 chr8: 144698291 . . . 144698872; − 1 TSTA3; CODING 2 3157677; 3157679 (100%); Block_5786 0.041931 chr5: 140907177 . . . 140908450; − 1 DIAPH1; CODING 3 2878674; 2878677; (100%); 2878678 Block_7480 0.041931 chr9: 133339512 . . . 133342185; + 1 ASS1; CODING 2 3191541; 3191544 (100%); Block_1345 0.042439 chr11: 117708078 . . . 117708992; − 1 FXYD6; CODING 2 3393486; 3393487 (50%); UTR (50%); Block_4268 0.042439 chr2: 219204527 . . . 219208304; + 1 PNKD; CODING 2 2527695; 2527701 (100%); Block_6945 0.042439 chr8: 74705646 . . . 74722855; − 1 UBE2W; CODING 3 3140775; 3140777; (33.33%); UTR 3140784 (66.66%); Block_7232 0.042439 chr9: 95043034 . . . 95050521; − 1 IARS; CODING 4 3214728; 3214733; (100%); 3214735; 3214738 Block_7440 0.042439 chr9: 100823070 . . . 100840627; + 1 NANS; CODING 3 3181467; 3181476; (100%); 3181477 Block_2098 0.042952 chr13: 32749690 . . . 32759246; + 1 FRY; CODING 2 3484547; 3484554 (100%); Block_3188 0.042952 chr17: 28770823 . . . 28794571; + 1 CPD; CODING 13 3716448; 3716452; (61.53%); UTR 3716456; 3716462; (38.46%); 3716464; 3716465; 3716467; 3716468; 3716469; 3716470; 3716471; 3716472; 3716473 Block_3884 0.042952 chr2: 162849805 . . . 162851512; − 1 DPP4; CODING 2 2584026; 2584027 (100%); Block_4710 0.042952 chr22: 45914565 . . . 45921519; + 1 FBLN1; CODING 2 3948657; 3948663 (100%); Block_5278 0.042952 chr4: 52890189 . . . 52896012; − 1 SGCB; CODING 2 2768987; 2768991 (100%); Block_5417 0.042952 chr4: 170057497 . . . 170077777; − 1 SH3RF1; CODING 3 2793167; 2793171; (100%); 2793172 Block_5452 0.042952 chr4: 15780104 . . . 15826604; + 1 CD38; CODING 4 2719662; 2719664; (100%); 2719672; 2719679 Block_6207 0.042952 chr6: 94120488 . . . 94124485; − 1 EPHA7; CODING 2 2965246; 2965247 (100%); Block_1773 0.043469 chr12: 118588359 . . . 118588947; − 1 TAOK3; CODING 3 3473806; 3473807; (66.66%); UTR 3473808 (33.33%); Block_298 0.043469 chr1: 154557366 . . . 154558321; − 1 ADAR; CODING 2 2436758; 2436762 (100%); Block_4455 0.043469 chr20: 37174997 . . . 37199484; + 1 RALGAPB; CODING 5 3884695; 3884701; (100%); 3884707; 3884708; 3884716 Block_5162 0.043469 chr3: 153973294 . . . 153975253; + 1 ARHGEF26; CODING 2 2648576; 2648579 (50%); UTR (50%); Block_5625 0.043469 chr4: 166301254 . . . 166375499; + 1 CPE; CODING 16 2750634; 2750635; (6.25%); UTR 2750636; 2750638; (12.5%); 2750639; 2750640; INTRONIC 2750642; 2750643; (81.25%); 2750680; 2750646; 2750647; 2750649; 2750650; 2750653; 2750655; 2750659 Block_6152 0.043469 chr6: 47199596 . . . 47199895; − 1 TNFRSF21; UTR (100%); 2 2956054; 2956055 Block_7360 0.043469 chr9: 140403604 . . . 140404196; − 1 PNPLA7; CODING 2 3231080; 3231081 (50%); INTRONIC (50%); Block_3670 0.043993 chr19: 51380028 . . . 51380127; + 1 KLK2; INTRONIC 2 3839576; 3839577 (100%); Block_4440 0.043993 chr20: 32232190 . . . 32236720; + 1 CBFA2T2; CODING 3 3882597; 3882598; (33.33%); UTR 3882603 (66.66%); Block_6431 0.043993 chr6: 106967344 . . . 106975345; + 1 AIM1; CODING 5 2919813; 2919814; (100%); 2919815; 2919816; 2919820 Block_7138 0.043993 chr8: 120255695 . . . 120257606; + 1 MAL2; ncTRANSCRIPT 3 3113192; 3113193; (100%); 3113194 Block_7231 0.043993 chr9: 95013006 . . . 95033327; − 1 IARS; CODING 7 3214701; 3214708; (100%); 3214713; 3214714; 3214716; 3214719; 3214721 Block_7316 0.043993 chr9: 136231716 . . . 136231744; − 1 SURF4; CODING 2 3228682; 4051974 (100%); Block_374 0.044521 chr1: 207102212 . . . 207112808; − 1 PIGR; CODING 11 2453007; 2453010; (90.90%); UTR 2453011; 2453012; (9.09%); 2453013; 2453015; 2453016; 2453018; 2453019; 2453020; 2453021 Block_4771 0.044521 chr3: 49062361 . . . 49062661; − 1 IMPDH2; CODING 2 2673881; 2673882 (100%); Block_6871 0.044521 chr8: 19261989 . . . 19277968; − 1 CSGALNACT1; CODING 5 3126508; 3126509; (80%); UTR 3126514; 3126520; (20%); 3126522 Block_3592 0.045055 chr19: 13264023 . . . 13264647; + 1 IER2; CODING 2 3822220; 3822222 (100%); Block_5515 0.045055 chr4: 79475596 . . . 79503433; + 1 ANXA3; CODING 2 2732851; 2732860 (50%); UTR (50%); Block_6620 0.045055 chr7: 99267347 . . . 99272139; − 1 CYP3A5; ncTRANSCRIPT 3 3063437; 3063444; (66.66%); 3063447 INTRONIC (33.33%); Block_1688 0.045594 chr12: 57648708 . . . 57650291; − 1 R3HDM2; CODING 2 3458457; 3458461 (100%); Block_2958 0.045594 chr17: 4175402 . . . 4186127; − 1 UBE2G1; UTR (100%); 2 3742072; 3742078 Block_698 0.045594 chr1: 178408557 . . . 178421750; + 1 RASAL2; CODING 4 2369197; 2369198; (100%); 2369199; 2369205 Block_7807 0.045594 chrX: 107923910 . . . 107923944; + 1 COL4A5; CODING 2 3986840; 4055605 (100%); Block_2251 0.046139 chr14: 76424744 . . . 76448197; − 1 TGFB3; INTERGENIC 11 3572518; 3572524; (9.09%); 3572528; 3572529; CODING 3572533; 3572534; (45.45%); UTR 3572539; 3572540; (45.45%); 3572541; 3572542; 3572543 Block_3274 0.046139 chr17: 59479110 . . . 59480539; + 1 TBX2; CODING 2 3729850; 3729852 (100%); Block_412 0.046139 chr1: 235643382 . . . 235658086; − 1 B3GALNT2; CODING 3 2461913; 2461914; (100%); 2461921 Block_2884 0.046689 chr16: 56969154 . . . 56977753; + 1 HERPUD1; CODING 10 3662393; 3662395; (80%); UTR 3662397; 3662401; (20%); 3662402; 3662403; 3662407; 3662408; 3662411; 3662412 Block_3707 0.046689 chr2: 10580851 . . . 10585351; − 1 ODC1; CODING 12 2540164; 2540166; (91.66%); UTR 2540167; 2540169; (8.33%); 2540171; 2540172; 2540173; 2540174; 2540175; 2540176; 2540178; 2540180 Block_6266 0.046689 chr6: 136990497 . . . 137041697; − 1 MAP3K5; CODING 6 2975930; 2975936; (100%); 2975938; 2975939; 2975940; 2975946 Block_947 0.046689 chr10: 95185842 . . . 95191270; − 1 MYOF; CODING 2 3300707; 3300708 (100%); Block_2092 0.047245 chr13: 26434339 . . . 26436545; + 1 ATP8A2; CODING 2 3482385; 3482388 (100%); Block_6585 0.047245 chr7: 51095830 . . . 51098577; − 1 COBL; CODING 3 3050639; 3050644; (100%); 3050648 Block_7357 0.047245 chr9: 140358830 . . . 140358908; − 1 PNPLA7; CODING 2 3231037; 4051814 (100%); Block_1824 0.047806 chr12: 12037385 . . . 12047640; + 1 ETV6; CODING 3 3405156; 3405162; (100%); 3405164 Block_439 0.047806 chr1: 11888539 . . . 11889339; + 1 CLCN6; CODING 3 2320500; 2320501; (100%); 2320502 Block_6029 0.047806 chr5: 148804031 . . . 148811072; + 1 RP11- INTERGENIC 8 2835105; 2835106; 394O4.2; (50%); 2835107; 2835108; ncTRANSCRIPT 2835111; 2835120; (50%); 2835124; 2835127 Block_6466 0.047806 chr6: 144724259 . . . 144768883; + 1 UTRN; CODING 8 2929201; 2929208; (100%); 2929210; 2929214; 2929215; 2929216; 2929223; 2929227 Block_1059 0.048945 chr10: 70728765 . . . 70741336; + 1 DDX21; CODING 5 3250074; 3250076; (100%); 3250079; 3250084; 3250086 Block_7268 0.048945 chr9: 114128562 . . . 114137482; − 1 KIAA0368; CODING 4 3220517; 3220518; (100%); 3220524; 3220527 Block_7643 0.048945 chrX: 114345684 . . . 114357459; − 1 LRCH2; CODING 2 4018756; 4018762 (50%); UTR (50%); Block_2984 0.049523 chr17: 26966940 . . . 26969094; − 1 KIAA0100; CODING 3 3750919; 3750921; (100%); 3750923

TABLE 23 ICE Category Block Wilcoxon Chromosomal # of Overlapping (Composition # of ID P-value Coordinates Genes Genes %) PSRs Probe set ID(s) Block_6592 0.000072 chr7: 37946647 . . . 37956059; − 1 SFRP4; CODING 9 3046448; 3046449; (66.66%); UTR 3046450; 3046457; (33.33%); 3046459; 3046460; 3046461; 3046462; 3046465; Block_4226 0.000089 chr2: 189863400 . . . 189867071; + 1 COL3A1; CODING 2 2519614; 2519620; (100%); Block_4627 0.000116 chr22: 29191774 . . . 29195014; − 1 XBP1; ncTRANSCRIPT 3 3956596; 3956601; (33.33%); 3956603; INTRONIC (66.66%); Block_6930 0.000183 chr8: 48649878 . . . 48650049; − 1 CEBPD; CODING 2 3134023; 3134024; (100%); Block_7113 0.00028 chr8: 75737169 . . . 75767196; + 1 PI15; CODING 16 3103704; 3103705; (43.75%); UTR 3103706; 3103707; (43.75%); 3103708; 3103710; INTRONIC 3103712; 3103713; (12.5%); 3103714; 3103715; 3103717; 3103718; 3103720; 3103721; 3103725; 3103726; Block_5470 0.000286 chr4: 15839733 . . . 15852471; + 1 CD38; INTERGENIC 5 2719689; 2719692; (20%); 2719694; 2719695; CODING 2719696; (60%); UTR (20%); Block_5155 0.000299 chr3: 132043108 . . . 132068493; + 1 ACPP; ncTRANSCRIPT 13 2642733; 2642735; (15.38%); 2642738; 2642739; INTRONIC 2642740; 2642741; (84.61%); 2642744; 2642745; 2642746; 2642747; 2642748; 2642750; 2642753; Block_3531 0.000313 chr19: 39897525 . . . 39899806; − 1 ZFP36; UTR_AS 4 3862010; 3862011; (25%); 3862006; 3862007; CODING_AS (75%); Block_1992 0.00032 chr13: 38158126 . . . 38166301; − 1 POSTN; CODING 4 3510098; 3510100; (100%); 3510101; 3510105; Block_4227 0.000372 chr2: 189867682 . . . 189873745; + 1 COL3A1; CODING 7 2519621; 2519623; (100%); 2519628; 2519629; 2519634; 2519637; 2519644; Block_5813 0.000424 chr5: 148880617 . . . 148880811; − 1 CTB- ncTRANSCRIPT 2 2880917; 2880918; 89H12.4; (100%); Block_6391 0.000433 chr6: 38840803 . . . 38841129; + 1 DNAH8; CODING 2 2906020; 2906021; (100%); Block_5469 0.000452 chr4: 15780104 . . . 15826604; + 1 CD38; CODING 4 2719662; 2719664; (100%); 2719672; 2719679; Block_1127 0.000595 chr10: 114710550 . . . 114711012; + 1 TCF7L2; CODING 2 3264623; 3264624; (100%); Block_6388 0.000634 chr6: 38783258 . . . 38783411; + 1 DNAH8; CODING 2 2905985; 2905986; (100%); Block_3521 0.000718 chr19: 18893864 . . . 18897074; − 1 COMP; CODING 2 3855221; 3855230; (100%); Block_2375 0.000812 chr14: 88553185 . . . 88560834; + 0 INTERGENIC 2 3547415; 3547424; (100%); Block_6389 0.000829 chr6: 38800098 . . . 38831738; + 1 DNAH8; CODING 14 2905993; 2905995; (100%); 2905996; 2905997; 2905999; 2906000; 2906001; 2906002; 2906003; 2906004; 2906005; 2906006; 2906010; 2906012; Block_2896 0.000846 chr16: 67202953 . . . 67203210; + 1 HSF4; CODING 2 3665255; 3665257; (100%); Block_1579 0.000882 chr12: 3718615 . . . 3753793; − 1 EFCAB4B; CODING 10 3440929; 3440999; (60%); 3441000; 3440930; INTRONIC 3440936; 3440938; (40%); 3440941; 3440942; 3440951; 3440952; Block_3687 0.000918 chr19: 53945049 . . . 53945553; + 1 CTD- ncTRANSCRIPT 2 3840864; 3840869; 2224J9.2; (100%); Block_3688 0.000957 chr19: 53957950 . . . 53961428; + 1 ZNF761; ncTRANSCRIPT 6 3840917; 3840921; (100%); 3840923; 3840935; 3840937; 3840939; Block_939 0.000996 chr10: 88820216 . . . 88820346; − 1 GLUD1; ncTRANSCRIPT 2 3298991; 4038370; (100%); Block_4225 0.001058 chr2: 189839219 . . . 189861926; + 1 COL3A1; CODING 15 2519583; 2519585; (100%); 2519586; 2519588; 2519589; 2519590; 2519595; 2519596; 2519598; 2519599; 2519601; 2519602; 2519604; 2519605; 2519610; Block_3653 0.001147 chr19: 41223728 . . . 41231316; + 1 ITPKC; CODING 5 3833738; 3833739; (80%); 3833740; 3833741; INTRONIC 3833743; (20%); Block_7267 0.001147 chr9: 99370376 . . . 99375212; − 1 CDC14B; INTRONIC 2 3216428; 3216429; (100%); Block_1991 0.00117 chr13: 38154719 . . . 38164537; − 1 POSTN; CODING 3 3510096; 3510097; (100%); 3510103; Block_3042 0.001292 chr17: 48262881 . . . 48277296; − 1 COL1A1; CODING 39 3762204; 3762206; (100%); 3762207; 3762208; 3762210; 3762211; 3762212; 3762215; 3762216; 3762217; 3762218; 3762220; 3762221; 3762222; 3762223; 3762225; 3762226; 3762227; 3762228; 3762229; 3762234; 3762235; 3762236; 3762238; 3762241; 3762242; 3762243; 3762244; 3762245; 3762246; 3762249; 3762252; 3762253; 3762254; 3762256; 3762257; 3762263; 3762264; 3762268; Block_6371 0.001345 chr6: 31785240 . . . 31797461; + 2 HSPA1B; CODING 2 2902713; 2902730; HSPA1A; (100%); Block_5279 0.001372 chr4: 40592576 . . . 40629213; − 1 RBM47; INTRONIC 4 2766856; 2766859; (100%); 2766860; 2766861; Block_3023 0.001455 chr17: 40538906 . . . 40539322; − 1 STAT3; INTRONIC 2 3757901; 3757902; (100%); Block_4139 0.001455 chr2: 101541626 . . . 101564800; + 1 NPAS2; CODING 4 2496436; 2496440; (100%); 2496446; 2496448; Block_2374 0.001605 chr14: 88550504 . . . 88559014; + 0 INTERGENIC 5 3547412; 3547413; (100%); 3547419; 3547420; 3547422; Block_3981 0.001605 chr2: 208628777 . . . 208631527; − 1 FZD5; UTR (100%); 4 2596768; 2596769; 2596771; 2596775; Block_7365 0.001636 chr9: 140354426 . . . 140354842; − 1 PNPLA7; UTR (100%); 2 3231011; 4051791; Block_6370 0.001701 chr6: 31795534 . . . 31795716; + 1 HSPA1B; CODING 2 2902726; 2902727; (100%); Block_6484 0.001701 chr6: 144635551 . . . 144635647; + 1 UTRN; INTRONIC 2 2929396; 2929397; (100%); Block_6152 0.001947 chr6: 35545311 . . . 35555083; − 1 FKBP5; INTRONIC 2 2951580; 2951584; (100%); Block_1926 0.001985 chr12: 102011150 . . . 102079590; + 1 MYBPC1; CODING 36 3428611; 3428612; (69.44%); UTR 3428613; 3428617; (2.77%); 3428619; 3428620; INTRONIC 3428623; 3428624; (27.77%); 3428625; 3428626; 3428627; 3428628; 3428629; 3428630; 3428631; 3428634; 3428635; 3428636; 3428637; 3428638; 3428639; 3428640; 3428641; 3428642; 3428643; 3428644; 3428646; 3428647; 3428648; 3428650; 3428651; 3428654; 3428655; 3428659; 3428665; 3428666; Block_4322 0.002062 chr2: 242135147 . . . 242164581; + 1 ANO7; CODING 24 2536222; 2536226; (91.66%); UTR 2536228; 2536229; (8.33%); 2536231; 2536232; 2536233; 2536234; 2536235; 2536236; 2536237; 2536238; 2536240; 2536241; 2536243; 2536245; 2536248; 2536249; 2536252; 2536253; 2536256; 2536260; 2536261; 2536262; Block_3449 0.002102 chr18: 56647020 . . . 56648694; + 1 ZNF532; INTRONIC 3 3790402; 3790403; (100%); 3790404; Block_1427 0.002268 chr11: 35166517 . . . 35193320; + 1 CD44; INTRONIC 2 3326642; 3326650; (100%); Block_3648 0.002312 chr19: 39897722 . . . 39899906; + 1 ZFP36; CODING 8 3832980; 3832981; (25%); UTR 3832982; 3832984; (12.5%); 3832985; 3832986; INTRONIC 3832987; 3832988; (62.5%); Block_2832 0.002493 chr16: 19433756 . . . 19439293; + 1 TMC5; INTRONIC 2 3650948; 3650949; (100%); Block_5745 0.002493 chr5: 86688587 . . . 86688721; − 1 CCNH; ncTRANSCRIPT 2 2865880; 2865881; (100%); Block_2304 0.002588 chr14: 38054451 . . . 38055847; + 0 INTERGENIC 4 3533031; 3533035; (100%); 3533037; 3533039; Block_1993 0.002637 chr13: 38158866 . . . 38162106; − 1 POSTN; CODING 2 3510099; 3510102; (100%); Block_6649 0.002687 chr7: 105893270 . . . 105922863; − 1 NAMPT; ncTRANSCRIPT 20 3066831; 3066833; (20%); 3066836; 3066837; INTRONIC 3066838; 3066839; (80%); 3066840; 3066841; 3066843; 3066844; 3066846; 3066847; 3066848; 3066849; 3066850; 3066853; 3066854; 3066859; 3066861; 3066862; Block_2897 0.002738 chr16: 67203603 . . . 67203747; + 1 HSF4; CODING 2 3665259; 3665260; (100%); Block_5232 0.002789 chr3: 186759705 . . . 186769256; + 1 ST6GAL1; ncTRANSCRIPT 3 2656853; 2656859; (66.66%); 2656860; UTR (33.33%); Block_1128 0.002895 chr10: 114723487 . . . 114732026; + 1 TCF7L2; INTRONIC 2 3264632; 3264636; (100%); Block_2631 0.002895 chr15: 78557858 . . . 78567151; + 1 DNAJA4; ncTRANSCRIPT 3 3603257; 3603266; (66.66%); 3603267; CODING (33.33%); Block_3099 0.002949 chr17: 76354002 . . . 76355176; − 1 SOCS3; CODING 4 3772289; 3772290; (25%); UTR 3772292; 3772293; (75%); Block_3597 0.003118 chr19: 12902599 . . . 12904034; + 1 JUNB; CODING 3 3821896; 3821898; (66.66%) UTR 3821899; (33.33%); Block_3448 0.003176 chr18: 56623078 . . . 56646570; + 1 ZNF532; INTRONIC 4 3790396; 3790398; (100%); 3790399; 3790401; Block_1429 0.003234 chr11: 35211649 . . . 35229188; + 1 CD44; ncTRANSCRIPT 15 3326671; 3326672; (40%); 3326674; 3326676; INTRONIC 3326677; 3326679; (60%); 3326680; 3326681; 3326684; 3326692; 3326695; 3326701; 3326703; 3326704; 3326708; Block_2471 0.003234 chr15: 55543544 . . . 55562575; − 1 RAB27A; UTR (50%); 2 3625289; 3625295; INTRONIC (50%); Block_2895 0.003294 chr16: 67199438 . . . 67201057; + 1 HSF4; ncTRANSCRIPT 5 3665235; 3665240; (20%); 3665244; 3665245; CODING 3665246; (80%); Block_1542 0.003355 chr11: 118379852 . . . 118380821; + 1 MLL; CODING 2 3351445; 3351446; (100%); Block_1185 0.003481 chr11: 3800418 . . . 3803305; − 1 NUP98; CODING 2 3359982; 3359983; (100%); Block_3591 0.003481 chr19: 11210844 . . . 11213743; + 1 LDLR; INTRONIC 2 3821020; 3821024; (100%); Block_4284 0.003676 chr2: 219676945 . . . 219679977; + 1 CYP27A1; CODING 7 2528108; 2528110; (85.71%); UTR 2528111; 2528112; (14.28%); 2528113; 2528115; 2528118; Block_834 0.003676 chr1: 247712494 . . . 247739511; + 1 C1orf150; CODING 3 2390125; 2390128; (66.66%); UTR 2390134; (33.33%); Block_1825 0.003743 chr12: 13350040 . . . 13366545; + 2 EMP1; UTR (50%); 6 3405757; 3405758; AC079628.1; INTRONIC 3405760; 3405766; (50%); 3405770; 3405772; Block_3512 0.003812 chr19: 15297695 . . . 15302661; − 1 NOTCH3; CODING 5 3853157; 3853158; (100%); 3853159; 3853161; 3853166; Block_4229 0.003812 chr2: 189875001 . . . 189877194; + 1 COL3A1; CODING 5 2519649; 2519652; (60%); UTR 2519656; 2519657; (40%); 2519658; Block_5137 0.003812 chr3: 121603566 . . . 121604258; + 1 EAF2; INTRONIC 2 2638711; 2638712; (100%); Block_5780 0.003952 chr5: 115146858 . . . 115148955; − 1 CDO1; CODING 2 2871912; 2871914; (100%); Block_5954 0.003952 chr5: 82785957 . . . 82786199; + 1 VCAN; CODING 2 2818532; 2818533; (100%); Block_1472 0.004024 chr11: 65273777 . . . 65273907; + 1 MALAT1; ncTRANSCRIPT 2 3335195; 3335196; (100%); Block_5764 0.004024 chr5: 95243613 . . . 95288598; − 1 ELL2; ncTRANSCRIPT 12 2867907; 2867915; (8.33%); 2867916; 2867924; INTRONIC 2867925; 2867926; (91.66%); 2867930; 2867931; 2867932; 2867934; 2867940; 2867941; Block_7742 0.004097 chrX: 23802057 . . . 23803407; + 1 SAT1; ncTRANSCRIPT 5 3971816; 3971817; (40%); 3971818; 3971820; CODING 3971821; (20%); UTR (40%); Block_5765 0.004247 chr5: 95257267 . . . 95259483; − 1 ELL2; INTRONIC 4 2867919; 2867921; (100%); 2867922; 2867923; Block_6642 0.004247 chr7: 99267347 . . . 99272139; − 1 CYP3A5; ncTRANSCRIPT 3 3063437; 3063444; (66.66%); 3063447; INTRONIC (33.33%); Block_4409 0.004324 chr20: 52560335 . . . 52561534; − 1 BCAS1; CODING 2 3910362; 3910363; (50%); UTR (50%); Block_7846 0.004324 chrX: 152770164 . . . 152773851; + 1 BGN; CODING 6 3995642; 3995651; (100%); 3995654; 3995657; 3995659; 3995661; Block_5950 0.004402 chr5: 79361251 . . . 79378964; + 1 THBS4; CODING 10 2817602; 2817603; (100%); 2817605; 2817606; 2817609; 2817611; 2817614; 2817615; 2817620; 2817621; Block_3980 0.004482 chr2: 208627560 . . . 208629500; − 1 FZD5; UTR (100%); 3 2596764; 2596765; 2596772; Block_7065 0.004482 chr8: 27398133 . . . 27402173; + 1 EPHX2; CODING 2 3091435; 3091442; (50%); UTR (50%); Block_2156 0.004562 chr13: 111940732 . . . 111953191; + 1 ARHGEF7; CODING 2 3501737; 3501744; (100%); Block_2613 0.004644 chr15: 71803346 . . . 71808234; + 1 THSD4; INTRONIC 2 3600358; 3600361; (100%); Block_4875 0.004644 chr3: 114412375 . . . 114429160; − 1 ZBTB20; UTR (100%); 2 2689628; 2689631; Block_1342 0.004727 chr11: 116914101 . . . 116935147; − 1 SIK3; INTRONIC 4 3393111; 3393112; (100%); 3393115; 3393116; Block_2614 0.004727 chr15: 71839666 . . . 71889637; + 1 THSD4; CODING 8 3600365; 3600366; (12.5%); UTR 3600482; 3600486; (12.5%); 3600368; 3600478; INTRONIC 3600371; 3600372; (75%); Block_2658 0.004727 chr15: 93482832 . . . 93486203; + 1 CHD2; CODING 2 3609197; 3609200; (100%); Block_3283 0.004727 chr17: 65027167 . . . 65028692; + 2 CACNG4; CODING 2 3732138; 3732139; AC005544.1; (50%); UTR (50%); Block_2002 0.004812 chr13: 45113061 . . . 45146842; − 1 TSC22D1; INTRONIC 7 3512332; 3512337; (100%); 3512338; 3512339; 3512341; 3512342; 3512344; Block_2833 0.004812 chr16: 19441750 . . . 19460940; + 1 TMC5; CODING 5 3650950; 3650954; (60%); UTR 3650955; 3650957; (40%); 3650958; Block_847 0.004812 chr10: 7392799 . . . 7409508; − 1 SFMBT2; INTRONIC 3 3276296; 3276241; (100%); 3276242; Block_1469 0.004898 chr11: 65191129 . . . 65191996; + 1 NEAT1; ncTRANSCRIPT 2 3335211; 3335215; (100%); Block_853 0.004986 chr10: 18874889 . . . 18903446; − 1 NSUN6; CODING 2 3280258; 3280265; (100%); Block_3682 0.005074 chr19: 51380028 . . . 51380127; + 1 KLK2; INTRONIC 2 3839576; 3839577; (100%); Block_3573 0.005165 chr19: 2476367 . . . 2477960; + 1 GADD45B; CODING 4 3816512; 3816515; (75%); UTR 3816519; 3816524; (25%); Block_4876 0.005165 chr3: 114435628 . . . 114450706; − 1 ZBTB20; INTRONIC 2 2689633; 2689638; (100%); Block_6288 0.005165 chr6: 143251252 . . . 143252058; − 1 HIVEP2; INTRONIC 2 2977329; 2977355; (100%); Block_6338 0.005165 chr6: 10556781 . . . 10566189; + 1 GCNT2; CODING 2 2894601; 2894610; (50%); INTRONIC (50%); Block_7142 0.005256 chr8: 102506747 . . . 102518399; + 1 GRHL2; INTRONIC 2 3109702; 3109705; (100%); Block_1361 0.005349 chr11: 122932160 . . . 122932410; − 1 HSPA8; UTR (100%); 2 3395451; 3395452; Block_2612 0.00554 chr15: 71716691 . . . 71716939; + 1 THSD4; INTRONIC 2 3600342; 3600343; (100%); Block_6390 0.00554 chr6: 38828265 . . . 38834650; + 1 DNAH8; CODING 2 2906008; 2906016; (100%); Block_748 0.00554 chr1: 203275102 . . . 203275613; + 1 BTG2; INTRONIC 3 2375667; 2375668; (100%); 2375670; Block_1894 0.005638 chr12: 69019900 . . . 69035432; + 1 RAP1B; INTRONIC 2 3421126; 3421130; (100%); Block_3970 0.005638 chr2: 201719352 . . . 201719803; − 1 CLK1; CODING 2 2594506; 2594508; (100%); Block_6229 0.005737 chr6: 99860469 . . . 99860591; − 1 SFRS18; CODING 2 2966287; 2966288; (100%); Block_2303 0.005838 chr14: 38033662 . . . 38058763; + 0 INTERGENIC 4 3533021; 3533028; (100%); 3533041; 3533045; Block_7715 0.005838 chrX: 2619960 . . . 2620197; + 1 CD99; INTRONIC 2 3966874; 4028497; (100%); Block_3508 0.006044 chr19: 12902622 . . . 12904019; − 1 JUNB; CODING_AS 2 3851771; 3851773; (100%); Block_6785 0.006044 chr7: 94028361 . . . 94059882; + 1 COL1A2; CODING 41 3013083; 3013086; (97.56%); UTR 3013095; 3013096; (2.43%); 3013098; 3013102; 3013103; 3013105; 3013106; 3013107; 3013109; 3013110; 3013111; 3013113; 3013114; 3013115; 3013116; 3013118; 3013119; 3013120; 3013124; 3013125; 3013127; 3013128; 3013129; 3013130; 3013135; 3013137; 3013139; 3013141; 3013142; 3013143; 3013146; 3013148; 3013151; 3013155; 3013156; 3013157; 3013158; 3013160; 3013161; Block_1771 0.00615 chr12: 118636857 . . . 118639157; − 1 TAOK3; CODING 2 3473836; 3473838; (100%); Block_4997 0.00615 chr3: 187460081 . . . 187461297; − 1 BCL6; INTRONIC 3 2709837; 2709817; (100%); 2709839; Block_2787 0.006477 chr16: 72984427 . . . 72992414; − 1 ZFHX3; CODING 2 3698340; 3698347; (100%); Block_4873 0.006477 chr3: 114353933 . . . 114405567; − 1 ZBTB20; INTRONIC 6 2689789; 2689794; (100%); 2689798; 2689807; 2689809; 2689776; Block_1826 0.006589 chr12: 13364471 . . . 13366481; + 1 EMP1; CODING 2 3405769; 3405771; (100%); Block_6318 0.006589 chr6: 160103692 . . . 160113602; − 1 SOD2; CODING 5 2982328; 2982330; (20%); UTR 2982332; 2982333; (40%); 2982335; INTRONIC (40%); Block_4898 0.006703 chr3: 120389279 . . . 120401114; − 1 HGD; CODING 6 2691446; 4047079; (66.66%); UTR 2691452; 4047076; (33.33%); 2691462; 4047071; Block_5788 0.006819 chr5: 131820117 . . . 131822522; − 1 IRF1; CODING 2 2875353; 2875362; (100%); Block_5824 0.006819 chr5: 151041302 . . . 151054230; − 1 SPARC; CODING 13 2882119; 2882120; (53.84%); UTR 2882121; 2882122; (46.15%); 2882123; 2882125; 2882128; 2882131; 2882133; 2882137; 2882139; 2882142; 2882143; Block_1145 0.006937 chr10: 123779283 . . . 123781483; + 1 TACC2; ncTRANSCRIPT 2 3268069; 3268071; (50%); UTR (50%); Block_3689 0.007178 chr19: 53959452 . . . 53959887; + 1 ZNF761; ncTRANSCRIPT 2 3840925; 3840931; (100%); Block_4535 0.007301 chr21: 36252858 . . . 36260789; − 1 RUNX1; CODING 3 3930427; 3930435; (33.33%); UTR 3930438; (66.66%); Block_5434 0.007682 chr4: 170137651 . . . 170167646; − 1 SH3RF1; INTRONIC 2 2793179; 2793181; (100%); Block_4228 0.007946 chr2: 189873814 . . . 189875606; + 1 COL3A1; CODING 3 2519645; 2519648; (100%); 2519653; Block_4973 0.007946 chr3: 156865888 . . . 156874463; − 1 CCNL1; ncTRANSCRIPT 14 2702330; 2702333; (35.71%); 2702335; 2702342; CODING 2702344; 2702345; (28.57%); UTR 2702346; 2702348; (21.42%); 2702352; 2702355; INTRONIC 2702356; 2702357; (14.28%); 2702358; 2702359; Block_5911 0.007946 chr5: 60648670 . . . 60667704; + 1 ZSWIM6; INTRONIC 4 2811300; 2811301; (100%); 2811302; 2811303; Block_2183 0.008081 chr14: 25325143 . . . 25326345; − 1 STXBP6; CODING 2 3558448; 3558449; (100%); Block_3447 0.008218 chr18: 56585564 . . . 56587447; + 1 ZNF532; CODING 3 3790379; 3790380; (100%); 3790381; Block_5955 0.008218 chr5: 82832827 . . . 82876595; + 1 VCAN; CODING 9 2818559; 2818561; (88.88%); UTR 2818568; 2818571; (11.11%); 2818572; 2818573; 2818577; 2818578; 2818582; Block_4412 0.008499 chr20: 52612441 . . . 52674693; − 1 BCAS1; CODING 3 3910385; 3910393; (100%); 3910394; Block_4770 0.008499 chr3: 39183443 . . . 39186746; − 1 CSRNP1; CODING 4 2669932; 2669935; (75%); UTR 2669936; 2669937; (25%); Block_3590 0.008642 chr19: 11210938 . . . 11241992; + 1 LDLR; CODING 17 3821022; 3821023; (100%); 3821026; 3821029; 3821031; 3821034; 3821035; 3821036; 3821037; 3821041; 3821042; 3821044; 3821045; 3821046; 3821048; 3821052; 3821054; Block_6089 0.008642 chr6: 2116070 . . . 2117790; − 1 GMDS; CODING 2 2938767; 2938771; (100%); Block_7272 0.008936 chr9: 110248037 . . . 110250537; − 1 KLF4; CODING 4 3219229; 3219230; (100%); 3219233; 3219235; Block_1183 0.009085 chr 11: 3792978 . . . 3793149; − 1 NUP98; CODING 2 3359975; 3359977; (100%); Block_1990 0.009085 chr13: 38137470 . . . 38138697; − 1 POSTN; CODING 2 3510070; 3510072; (100%); Block_4411 0.009085 chr20: 52574002 . . . 52601991; − 1 BCAS1; CODING 3 3910367; 3910373; (100%); 3910378; Block_6454 0.009085 chr6: 108942915 . . . 108943132; + 1 FOXO3; INTRONIC 2 2920517; 2920518; (100%); Block_6540 0.009085 chr6: 160770298 . . . 160864773; + 2 AL591069.1; ncTRANSCRIPT 29 2934526; 2934527; SLC22A3; (3.44%); 2934531; 2934533; CODING 2934535; 2934580; (27.58%); 2934582; 2934585; INTRONIC 2934586; 2934536; (68.96%); 2934537; 2934538; 2934539; 2934541; 2934543; 2934545; 2934547; 2934548; 2934549; 2934550; 2934551; 2934554; 2934556; 2934557; 2934558; 2934559; 2934560; 2934561; 2934562; Block_7064 0.009085 chr8: 27382879 . . . 27399020; + 1 EPHX2; CODING 3 3091429; 3091433; (100%); 3091436; Block_5912 0.009238 chr5: 60699060 . . . 60705963; + 1 ZSWIM6; INTRONIC 2 2811311; 2811314; (100%); Block_6369 0.009238 chr6: 31785537 . . . 31785681; + 1 HSPA1A; UTR (100%); 2 2902715; 2902716; Block_2101 0.009392 chr13: 41890982 . . . 41891060; + 1 NAA16; CODING 2 3486890; 3486891; (100%); Block_6340 0.009392 chr6: 10697570 . . . 10707720; + 1 PAK1IP1; CODING 6 2894670; 2894671; (100%); 2894673; 2894676; 2894677; 2894681; Block_6407 0.009392 chr6: 44752539 . . . 44800262; + 1 SUPT3H; INTRONIC_AS 3 2908668; 2908682; (33.33%); 2908684; INTERGENIC (33.33%); CODING_AS (33.33%); Block_7862 0.009392 chrY: 21186129 . . . 21189006; − 1 NCRNA00185; INTRONIC 2 4035800; 4035801; (100%); Block_1729 0.009708 chr12: 103234188 . . . 103249107; − 1 PAH; CODING 3 3468486; 3468494; (100%); 3468504; Block_4002 0.009708 chr2: 227657803 . . . 227659434; − 1 IRS1; INTRONIC 2 2602032; 2602033; (100%); Block_6542 0.009708 chr6: 160868751 . . . 160872088; + 1 SLC22A3; CODING 2 2934572; 2934575; (100%); Block_1913 0.009869 chr12: 93774378 . . . 93775567; + 1 NUDT4; INTRONIC 3 3426176; 3426178; (100%); 3426180; Block_4996 0.010033 chr3: 187457927 . . . 187458752; − 1 BCL6; INTRONIC 2 2709814; 2709815; (100%); Block_6541 0.010033 chr6: 160866011 . . . 160868068; + 1 SLC22A3; INTRONIC 3 2934564; 2934565; (100%); 2934567; Block_7019 0.010033 chr8: 135820790 . . . 135827602; − 0 INTERGENIC 2 3154820; 3154823; (100%); Block_5230 0.010199 chr3: 186696431 . . . 186720502; + 1 ST6GAL1; UTR (33.33%); 3 2656910; 2656906; INTRONIC 2656846; (66.66%); Block_4974 0.010368 chr3: 156866425 . . . 156867848; − 1 CCNL1; ncTRANSCRIPT 2 2702334; 2702341; (100%); Block_5229 0.010368 chr3: 186656184 . . . 186662034; + 1 ST6GAL1; INTRONIC 2 2656876; 2656884; (100%); Block_2155 0.010539 chr13: 111932910 . . . 111938586; + 1 ARHGEF7; CODING 2 3501728; 3501736; (100%); Block_5727 0.010539 chr5: 68588077 . . . 68595899; − 1 CCDC125; CODING 2 2860627; 2860632; (100%); Block_4872 0.010713 chr3: 114311442 . . . 114318066; − 1 ZBTB20; INTRONIC 3 2689598; 2689599; (100%); 2689601; Block_6449 0.010713 chr6: 106967344 . . . 106975345; + 1 AIM1; CODING 5 2919813; 2919814; (100%); 2919815; 2919816; 2919820; Block_1048 0.010889 chr10: 51550046 . . . 51562146; + 1 MSMB; ncTRANSCRIPT 9 3246410; 3246413; (22.22%); 3246427; 3246428; INTRONIC 3246429; 3246430; (77.77%); 3246431; 3246414; 3246415; Block_2517 0.010889 chr15: 66072454 . . . 66076243; − 1 DENND4A; INTRONIC 2 3629917; 3629918; (100%); Block_6088 0.010889 chr6: 1930342 . . . 1961193; − 1 GMDS; CODING 3 2938731; 2938739; (100%); 2938741; Block_7063 0.010889 chr8: 27358443 . . . 27380016; + 1 EPHX2; CODING 6 3091408; 3091410; (100%); 3091412; 3091414; 3091418; 3091427; Block_6674 0.011068 chr7: 130764976 . . . 130789833; − 1 AC058791.2; ncTRANSCRIPT 6 3072944; 3072948; (16.66%); 3072856; 3072860; INTRONIC 3072861; 3072863; (83.33%); Block_7280 0.011068 chr9: 112963294 . . . 112963740; − 1 C9orf152; CODING 3 3220143; 3220147; (100%); 3220149; Block_1093 0.011249 chr10: 93702200 . . . 93713592; + 1 BTAF1; CODING 2 3257953; 3257956; (100%); Block_1783 0.011249 chr12: 123212329 . . . 123213804; − 1 GPR81; UTR (100%); 2 3475776; 3475778; Block_1262 0.011433 chr11: 62559948 . . . 62563808; − 1 NXF1; CODING 5 3376159; 3376162; (100%); 3376163; 3376165; 3376169; Block_2366 0.011433 chr14: 73572725 . . . 73572938; + 1 RBM25; CODING 2 3543443; 3543444; (100%); Block_4428 0.01162 chr20: 6004032 . . . 6005887; + 1 CRLS1; ncTRANSCRIPT 2 3875259; 3875261; (50%); INTRONIC (50%); Block_4828 0.01162 chr3: 71080277 . . . 71088814; − 1 FOXP1; INTRONIC 4 2681951; 2681956; (100%); 2681814; 2681815; Block_7607 0.01181 chrX: 67413739 . . . 67518927; − 1 OPHN1; CODING 6 4011226; 4011231; (100%); 4011234; 4011241; 4011242; 4011244; Block_2003 0.012595 chr13: 45147330 . . . 45150071; − 1 TSC22D1; CODING 8 3512345; 3512347; (100%); 3512348; 3512350; 3512351; 3512352; 3512354; 3512355; Block_4410 0.012595 chr20: 52571654 . . . 52574704; − 1 BCAS1; INTRONIC 2 3910366; 3910368; (100%); Block_6796 0.012595 chr7: 99169875 . . . 99170304; + 1 ZNF655; CODING 2 3014925; 3014926; (100%); Block_1572 0.012798 chr11: 134147231 . . . 134188819; + 1 GLB1L3; CODING 13 3357348; 3357349; (100%); 3357360; 3357363; 3357369; 3357370; 3357371; 3357375; 3357382; 3357383; 3357384; 3357386; 3357387; Block_4895 0.013005 chr3: 120363705 . . . 120364125; − 1 HGD; INTRONIC 2 2691410; 4047097; (100%); Block_6178 0.013005 chr6: 53200331 . . . 53207275; − 2 ELOVL5; ncTRANSCRIPT 4 2957648; 2957651; RP3- (50%); 2957653; 2957655; 483K16.2; INTRONIC (50%); Block_6315 0.013005 chr6: 159216475 . . . 159227934; − 1 EZR; INTRONIC 2 2981955; 2981961; (100%); Block_4534 0.013214 chr21: 36238786 . . . 36251434; − 1 RUNX1; INTRONIC 5 3930422; 3930512; (100%); 3930426; 3930520; 3930522; Block_7689 0.013214 chrX: 138182745 . . . 138221675; − 1 FGF13; INTRONIC 2 4024065; 4023962; (100%); Block_1415 0.013426 chr11: 32953313 . . . 32976949; + 1 QSER1; CODING 4 3325783; 3325784; (100%); 3325787; 3325791; Block_6641 0.013426 chr7: 99250225 . . . 99260505; − 1 CYP3A5; CODING 2 3063412; 3063422; (100%); Block_1720 0.013641 chr12: 93959391 . . . 93960697; − 1 AC025260.2; ncTRANSCRIPT 2 3465863; 3465865; (50%); INTRONIC (50%); Block_1827 0.01386 chr12: 13366615 . . . 13369004; + 1 EMP1; CODING 3 3405774; 3405777; (66.66%); UTR 3405778; (33.33%); Block_7001 0.01386 chr8: 116555732 . . . 116584992; − 1 TRPS1; INTRONIC 3 3149560; 3149563; (100%); 3149566; Block_4874 0.014081 chr3: 114406132 . . . 114412366; − 1 ZBTB20; ncTRANSCRIPT 5 2689618; 2689620; (20%); 2689621; 2689622; INTRONIC 2689627; (80%); Block_6329 0.014081 chr6: 170594681 . . . 170595380; − 1 DLL1; CODING 2 2986376; 2986377; (100%); Block_3850 0.014305 chr2: 100484261 . . . 100509150; − 1 AFF3; INTRONIC 2 2567082; 2567086; (100%); Block_1603 0.014533 chr12: 10856860 . . . 10871920; − 1 CSDA; ncTRANSCRIPT 12 3444265; 3444266; (50%); 3444274; 3444275; INTRONIC 3444280; 3444281; (50%); 3444283; 3444286; 3444287; 3444288; 3444289; 3444291; Block_3511 0.014533 chr19: 14626171 . . . 14627750; − 1 DNAJB1; CODING 3 3852788; 3852789; (33.33%); UTR 3852793; (66.66%); Block_4890 0.014533 chr3: 120347285 . . . 120347311; − 1 HGD; CODING 2 2691370; 4047116; (100%); Block_2332 0.014998 chr14: 60398687 . . . 60411444; + 1 LRRC9; ncTRANSCRIPT 2 3538417; 3538420; (100%); Block_5744 0.014998 chr5: 86682116 . . . 86683398; − 1 RASA1; INTRONIC_AS 2 2865872; 2865875; (100%); Block_601 0.014998 chr1: 104076371 . . . 104078044; + 1 RNPC3; CODING 3 2349363; 2349364; (100%); 2349365; Block_7215 0.014998 chr9: 73021937 . . . 73022490; − 1 KLF9; INTRONIC 2 3209008; 3209009; (100%); Block_7366 0.014998 chr9: 140354863 . . . 140355186; − 1 PNPLA7; CODING 4 3231012; 4051792; (100%); 3231015; 4051795; Block_7376 0.014998 chr9: 140437902 . . . 140444736; − 1 PNPLA7; CODING 4 3231109; 3231112; (75%); UTR 3231115; 3231117; (25%); Block_7441 0.015235 chr9: 92219943 . . . 92220976; + 1 GADD45G; CODING 5 3178680; 3178681; (80%); UTR 3178683; 3178685; (20%); 3178687; Block_5812 0.015476 chr5: 148876962 . . . 148929959; − 2 CTB- CODING 21 2880949; 2880951; 89H12.4; (9.52%); 2880958; 2880960; CSNK1A1; ncTRANSCRIPT 2880964; 2880968; (14.28%); 2880973; 2880983; UTR (9.52%); 2880985; 2880889; INTRONIC 2880890; 2880987; (66.66%); 2880892; 2880893; 2880896; 2880901; 2880989; 2880991; 2880993; 2880995; 2880997; Block_6536 0.015476 chr6: 160174501 . . . 160176484; + 1 WTAP; CODING 2 2934120; 2934122; (100%); Block_4877 0.015719 chr3: 114455332 . . . 114550610; − 1 ZBTB20; UTR (9.09%); 11 2689639; 2689640; INTRONIC 2689641; 2689647; (90.90%); 2689655; 2689824; 2689825; 2689826; 2689829; 2689658; 2689838; Block_4892 0.015719 chr3: 120352074 . . . 120352166; − 1 HGD; CODING 2 2691378; 4047112; (100%); Block_5014 0.015719 chr3: 196118688 . . . 196120490; − 1 UBXN7; CODING 2 2712875; 2712876; (100%); Block_7724 0.015719 chrX: 2653716 . . . 2653766; + 1 CD99; INTRONIC 2 3966880; 4028503; (100%); Block_4041 0.015967 chr2: 14775429 . . . 14775897; + 1 FAM84A; UTR (100%); 2 2470490; 2470491; Block_4759 0.016217 chr3: 27490249 . . . 27493978; − 1 SLC4A7; CODING 2 2666957; 2666959; (100%); Block_5187 0.016217 chr3: 156395446 . . . 156424304; + 1 TIPARP; CODING 12 2649140; 2649141; (75%); UTR 2649142; 2649149; (25%); 2649150; 2649151; 2649152; 2649154; 2649155; 2649156; 2649158; 2649160; Block_5728 0.016217 chr5: 68581172 . . . 68599751; − 1 CCDC125; CODING 2 2860623; 2860634; (100%); Block_2879 0.016472 chr16: 56667710 . . . 56678081; + 4 MT1JP; ncTRANSCRIPT 5 3662156; 3662163; MT1DP; (20%); 3662122; 3662124; MT1M; CODING 3662175; MT1A; (80%); Block_3849 0.016472 chr2: 100426047 . . . 100692345; − 1 AFF3; CODING 61 2566957; 2566960; (6.55%); 2566961; 2566965; ncTRANSCRIPT 2566966; 2566971; (3.27%); 2567075; 2567076; INTRONIC 2567084; 2567063; (90.16%); 2566976; 2567087; 2567088; 2566977; 2567064; 2567097; 2567067; 2567069; 2567101; 2567103; 2567071; 2566979; 2566982; 2566983; 2566984; 2566985; 2567105; 2567111; 2567113; 2567115; 2567106; 2566987; 2566988; 2566991; 2566993; 2566994; 2566996; 2566997; 2567121; 2566998; 2567125; 2567000; 2567001; 2567002; 2567003; 2567005; 2567007; 2567008; 2567010; 2567011; 2567012; 2567013; 2567014; 2567015; 2567017; 2567018; 2567019; 2567020; 2567022; 2567023; 2567127; Block_4871 0.016472 chr3: 114304388 . . . 114307096; − 1 ZBTB20; INTRONIC 2 2689592; 2689595; (100%); Block_7460 0.016472 chr9: 102594989 . . . 102628250; + 1 NR4A3; CODING 5 3182004; 3182005; (80%); UTR 3182010; 3182012; (20%); 3182015; Block_7690 0.016472 chrX: 138283258 . . . 138284475; − 1 FGF13; INTRONIC 2 4023972; 4023973; (100%); Block_5769 0.016729 chr5: 98208150 . . . 98209408; − 1 CHD1; CODING 2 2868550; 2868554; (100%); Block_6035 0.01699 chr5: 142273810 . . . 142281592; + 1 ARHGAP26; CODING 2 2833347; 2833348; (100%); Block_2057 0.017255 chr13: 107220269 . . . 107220463; − 1 ARGLU1; UTR (100%); 2 3524638; 3524639; Block_4034 0.017255 chr2: 10133339 . . . 10136095; + 1 GRHL1; CODING 2 2469190; 2469193; (100%); Block_5298 0.017255 chr4: 66465162 . . . 66468022; − 1 EPHA5; CODING 3 2771409; 2771411; (66.66%); 2771412; INTRONIC (33.33%); Block_6483 0.017255 chr6: 144615778 . . . 144641963; + 1 UTRN; INTRONIC 4 2929179; 2929184; (100%); 2929185; 2929186; Block_2666 0.017523 chr15: 99256649 . . . 99277206; + 1 IGF1R; INTRONIC 2 3610818; 3610825; (100%); Block_2758 0.017523 chr16: 56701878 . . . 56701935; − 1 MT1G; CODING 2 3693007; 3693008; (50%); UTR (50%); Block_4893 0.017523 chr3: 120357311 . . . 120357397; − 1 HGD; CODING 2 2691386; 4047108; (100%); Block_6349 0.017523 chr6: 18392721 . . . 18401507; + 1 RNF144B; INTRONIC 2 2897184; 2897227; (100%); Block_7144 0.017523 chr8: 102593447 . . . 102596253; + 1 GRHL2; INTRONIC 2 3109729; 3109731; (100%); Block_1049 0.017795 chr10: 51562272 . . . 51562497; + 1 MSMB; CODING 2 3246417; 3246418; (50%); UTR (50%); Block_2195 0.017795 chr14: 38041009 . . . 38048612; − 0 INTERGENIC 2 3561714; 3561715; (100%); Block_2885 0.017795 chr16: 56975974 . . . 56977926; + 1 HERPUD1; INTERGENIC 2 3662406; 3662413; (50%); INTRONIC (50%); Block_3767 0.017795 chr2: 43793837 . . . 43793938; − 1 THADA; CODING 2 2550679; 2550680; (100%); Block_3865 0.017795 chr2: 121999944 . . . 122005845; − 1 TFCP2L1; CODING 3 2573617; 2573621; (100%); 2573622; Block_5763 0.017795 chr5: 95242076 . . . 95243501; − 1 ELL2; INTRONIC 2 2867901; 2867906; (100%); Block_6151 0.017795 chr6: 35542614 . . . 35588051; − 1 FKBP5; CODING 10 2951575; 2951576; (80%); UTR 2951579; 2951581; (20%); 2951583; 2951587; 2951589; 2951593; 2951595; 2951596; Block_1340 0.018071 chr11: 115219890 . . . 115222358; − 1 CADM1; INTRONIC 2 3392454; 3392441; (100%); Block_3892 0.018071 chr2: 160303401 . . . 160304888; − 1 BAZ2B; CODING 2 2583084; 2583085; (100%); Block_4924 0.018071 chr3: 129123093 . . . 129137223; − 1 C3orf25; CODING 2 2694763; 2694771; (100%); Block_626 0.018071 chr1: 116933667 . . . 116939211; + 1 ATP1A1; INTRONIC 4 2353509; 2353512; (100%); 2353513; 2353517; Block_3422 0.018351 chr18: 39623725 . . . 39629533; + 1 PIK3C3; CODING 2 3786127; 3786129; (100%); Block_3756 0.018351 chr2: 38975252 . . . 38976820; − 1 SRSF7; CODING 5 2548982; 2548985; (80%); UTR 2548989; 2548990; (20%); 2548993; Block_4870 0.018351 chr3: 114214433 . . . 114219034; − 1 ZBTB20; INTRONIC 5 2689743; 2689744; (100%); 2689754; 2689756; 2689758; Block_5617 0.018634 chr4: 148786000 . . . 148787937; + 1 ARHGAP10; CODING 2 2746731; 2746736; (100%); Block_5746 0.018634 chr5: 86686709 . . . 86690299; − 2 CCNH; CODING 2 2865878; 2865887; RASA1; (50%); UTR_AS (50%); Block_7422 0.018634 chr9: 75773460 . . . 75785150; + 1 ANXA1; CODING 11 3174830; 3174831; (90.90%); UTR 3174835; 3174838; (9.09%); 3174840; 3174845; 3174847; 3174850; 3174853; 3174856; 3174857; Block_3565 0.019212 chr19: 863256 . . . 863423; + 1 CFD; UTR (100%); 2 3815252; 3815253; Block_4878 0.019507 chr3: 114465255 . . . 114510905; − 1 ZBTB20; ncTRANSCRIPT 7 2689643; 2689645; (14.28%); 2689646; 2689649; INTRONIC 2689651; 2689654; (85.71%); 2689656; Block_5523 0.019507 chr4: 77512391 . . . 77515089; + 1 SHROOM3; INTRONIC 2 2732215; 2732103; (100%); Block_7219 0.019507 chr9: 74360664 . . . 74362413; − 1 TMEM2; INTRONIC 2 3209449; 3209451; (100%); Block_745 0.019507 chr1: 201980268 . . . 201985198; + 1 ELF3; CODING 9 2375017; 2375020; (77.77%); UTR 2375022; 2375027; (22.22%); 2375028; 2375031; 2375033; 2375034; 2375035; Block_4894 0.019805 chr3: 120357401 . . . 120369669; − 1 HGD; ncTRANSCRIPT 22 2691388; 4047107; (9.09%); 2691394; 4047105; CODING 2691396; 4047104; (63.63%); 2691400; 4047102; INTRONIC 2691404; 4047100; (27.27%); 2691406; 4047099; 2691408; 4047098; 2691414; 4047095; 2691416; 4047094; 2691418; 4047093; 2691420; 4047092; Block_7368 0.019805 chr9: 140356003 . . . 140357262; − 1 PNPLA7; CODING 6 3231020; 4051802; (100%); 3231024; 4051804; 3231029; 4051807; Block_1146 0.020108 chr10: 123988023 . . . 123990167; + 1 TACC2; CODING 3 3268174; 3268175; (33.33%); 3268178; INTRONIC (66.66%); Block_1818 0.020108 chr12: 11836357 . . . 11863628; + 1 ETV6; INTRONIC 4 3405070; 3405071; (100%); 3405073; 3405079; Block_2437 0.020415 chr15: 42445498 . . . 42446391; − 1 PLA2G4F; CODING 2 3620449; 3620451; (100%); Block_334 0.020415 chr1: 169693470 . . . 169702101; − 1 SELE; CODING 11 2443481; 2443482; (81.81%); UTR 2443486; 2443489; (18.18%); 2443490; 2443492; 2443494; 2443495; 2443496; 2443499; 2443501; Block_7796 0.020415 chrX: 70775823 . . . 70776629; + 1 OGT; CODING 2 3981142; 3981144; (100%); Block_1893 0.020726 chr12: 69006519 . . . 69013759; + 1 RAP1B; INTRONIC 3 3421121; 3421122; (100%); 3421123; Block_5714 0.020726 chr5: 58481017 . . . 58511763; − 1 PDE4D; CODING 4 2858211; 2858215; (100%); 2858221; 2858222; Block_5866 0.020726 chr5: 180278404 . . . 180278437; − 1 ZFP62; CODING 2 2890930; 4047645; (100%); Block_4533 0.02104 chr21: 36193606 . . . 36197820; − 1 RUNX1; UTR (50%); 2 3930392; 3930397; INTRONIC (50%); Block_6626 0.02104 chr7: 87910829 . . . 87912896; − 1 STEAP4; UTR (50%); 2 3060345; 3060349; INTRONIC (50%); Block_3539 0.02136 chr19: 45016075 . . . 45029277; − 1 CEACAM20; ncTRANSCRIPT 8 3864953; 3864956; (100%); 3864957; 3864959; 3864961; 3864962; 3864964; 3864967; Block_3551 0.02136 chr19: 51410040 . . . 51412584; − 1 KLK4; CODING 7 3868736; 3868737; (85.71%); UTR 3868738; 3868740; (14.28%); 3868741; 3868743; 3868745; Block_3894 0.02136 chr2: 160885361 . . . 160898634; − 1 PLA2R1; CODING 3 2583439; 2583441; (100%); 2583443; Block_2870 0.021683 chr16: 53260310 . . . 53269212; + 1 CHD9; CODING 2 3660920; 3660927; (100%); Block_6631 0.021683 chr7: 95213206 . . . 95224446; − 1 PDK4; CODING 12 3062083; 3062084; (91.66%); UTR 3062087; 3062089; (8.33%); 3062091; 3062096; 3062099; 3062100; 3062102; 3062103; 3062105; 3062108; Block_5961 0.02201 chr5: 95087958 . . . 95103870; + 1 RHOBTB3; CODING 3 2820942; 2820947; (100%); 2820954; Block_6316 0.02201 chr6: 159222851 . . . 159229779; − 1 EZR; INTRONIC 2 2981957; 2981963; (100%); Block_6886 0.02201 chr8: 17573279 . . . 17612789; − 1 MTUS1; CODING 4 3125964; 3125967; (100%); 3125973; 3125975; Block_1915 0.022342 chr12: 93968968 . . . 93969774; + 1 SOCS2; UTR (100%); 4 3426279; 3426280; 3426281; 3426282; Block_1263 0.022679 chr11: 62568586 . . . 62571024; − 1 NXF1; CODING 3 3376178; 3376180; (100%); 3376187; Block_7564 0.022679 chrX: 11369976 . . . 11398542; − 1 ARHGAP6; INTRONIC 2 3999693; 3999639; (100%); Block_7605 0.022679 chrX: 67272384 . . . 67284017; − 1 OPHN1; CODING 2 4011206; 4011209; (100%); Block_4815 0.023019 chr3: 64630315 . . . 64636668; − 1 ADAMTS9; UTR (50%); 2 2680133; 2680139; INTRONIC (50%); Block_5174 0.023019 chr3: 150128646 . . . 150129079; + 1 TSC22D2; CODING 2 2647664; 2647665; (100%); Block_3214 0.023364 chr17: 39969468 . . . 39976700; + 1 FKBP10; CODING 5 3721456; 3721461; (100%); 3721462; 3721465; 3721472; Block_3456 0.023714 chr18: 59958780 . . . 59972846; + 1 KIAA1468; CODING 4 3791229; 3791231; (75%); UTR 3791236; 3791237; (25%); Block_4141 0.024068 chr2: 102781282 . . . 102792104; + 1 IL1R1; CODING 7 2497000; 2497001; (100%); 2497002; 2497004; 2497007; 2497010; 2497012; Block_6797 0.024068 chr7: 99169519 . . . 99170579; + 1 ZNF655; CODING 2 3014924; 3014928; (50%); INTRONIC (50%); Block_7300 0.024068 chr9: 124124355 . . . 124128420; − 1 STOM; UTR (50%); 2 3223950; 3223954; INTRONIC (50%); Block_1042 0.024426 chr10: 43615579 . . . 43622087; + 1 RET; CODING 3 3243877; 3243878; (100%); 3243881; Block_3534 0.024426 chr19: 40540451 . . . 40540826; − 1 ZNF780B; CODING 2 3862345; 3862347; (100%); Block_3668 0.024426 chr19: 49377023 . . . 49378997; + 1 PPP1R15A; CODING 4 3838008; 3838010; (100%); 3838011; 3838013; Block_4625 0.024426 chr22: 29190562 . . . 29191698; − 1 XBP1; CODING 3 3956591; 3956593; (66.66%); UTR 3956594; (33.33%); Block_6815 0.024426 chr7: 104749510 . . . 104750810; + 1 MLL5; CODING 2 3017637; 3017638; (100%); Block_37 0.024789 chr1: 8072266 . . . 8082267; − 1 ERRFI1; ncTRANSCRIPT 10 2395182; 2395184; (10%); 2395187; 2395188; CODING 2395189; 2395190; (30%); UTR 2395191; 2395192; (30%); 2395193; 2395195; INTRONIC (30%); Block_5138 0.024789 chr3: 121615255 . . . 121660380; + 1 SLC15A2; CODING 21 2638732; 2638733; (90.47%); UTR 2638734; 2638735; (9.52%); 2638737; 2638738; 2638742; 2638743; 2638744; 2638745; 2638746; 2638749; 2638750; 2638751; 2638754; 2638756; 2638757; 2638758; 2638760; 2638761; 2638762; Block_6328 0.024789 chr6: 169616207 . . . 169620400; − 1 THBS2; CODING 6 2985811; 2985812; (16.66%); UTR 2985813; 2985814; (83.33%); 2985815; 2985816; Block_6611 0.024789 chr7: 75721390 . . . 75729255; − 1 AC005077.12; ncTRANSCRIPT 2 3057596; 3057600; (100%); Block_940 0.024789 chr10: 88848954 . . . 88853651; − 1 GLUD1; UTR (60%); 5 3299016; 4038350; INTRONIC 3299019; 3299020; (40%); 3299022; Block_2230 0.025157 chr14: 69421708 . . . 69430379; − 1 ACTN1; INTRONIC 2 3569890; 3569894; (100%); Block_2275 0.025157 chr14: 102548195 . . . 102552551; − 1 HSP90AA1; INTRONIC 6 3580183; 3580189; (100%); 3580195; 3580199; 3580201; 3580206; Block_2928 0.025157 chr16: 84910468 . . . 84914235; + 1 CRISPLD2; INTRONIC 2 3671967; 3671971; (100%); Block_7374 0.025157 chr9: 140375422 . . . 140389574; − 1 PNPLA7; CODING 3 3231051; 3231059; (100%); 3231063; Block_949 0.025157 chr10: 95066684 . . . 95066750; − 1 MYOF; CODING 2 3300605; 3300606; (50%); UTR (50%); Block_2931 0.02553 chr16: 89758258 . . . 89759855; + 1 CDK10; CODING 3 3674319; 3674324; (100%); 3674326; Block_4003 0.02553 chr2: 227661614 . . . 227662290; − 1 IRS1; CODING 2 2602044; 2602045; (100%); Block_4979 0.02553 chr3: 160803580 . . . 160804455; − 1 B3GALNT1; CODING 2 2703388; 2703390; (100%); Block_2786 0.025907 chr16: 72827353 . . . 72832458; − 1 ZFHX3; CODING 2 3698277; 3698282; (100%); Block_3856 0.025907 chr2: 106005706 . . . 106013825; − 1 FHL2; INTRONIC 2 2568719; 2568727; (100%); Block_5618 0.025907 chr4: 148800406 . . . 148834290; + 1 ARHGAP10; CODING 2 2746744; 2746753; (100%); Block_6861 0.025907 chr7: 139083359 . . . 139090458; + 1 LUC7L2; CODING 3 3027013; 3027014; (100%); 3027015; Block_2302 0.026289 chr14: 38038123 . . . 38038868; + 0 INTERGENIC 2 3533022; 3533023; (100%); Block_5707 0.026289 chr5: 54786572 . . . 54830000; − 1 PPAP2A; INTRONIC 8 2857242; 2857264; (100%); 2857273; 2857275; 2857280; 2857282; 2857246; 2857254; Block_3672 0.026677 chr19: 49606718 . . . 49606842; + 1 SNRNP70; UTR (100%); 2 3838212; 3838213; Block_4626 0.026677 chr22: 29192148 . . . 29195118; − 1 XBP1; CODING 3 3956598; 3956600; (100%); 3956604; Block_4994 0.026677 chr3: 185643370 . . . 185644451; − 1 TRA2B; CODING 2 2709093; 2709095; (100%); Block_7143 0.026677 chr8: 102555510 . . . 102565001; + 1 GRHL2; CODING 2 3109712; 3109716; (100%); Block_3735 0.027069 chr2: 24535214 . . . 24536392; − 1 ITSN2; CODING 2 2544325; 2544328; (100%); Block_3847 0.027069 chr2: 100372047 . . . 100415240; − 1 AFF3; INTRONIC 5 2566941; 2566942; (100%); 2566948; 2566949; 2566955; Block_4519 0.027069 chr21: 29811695 . . . 29818793; − 1 AF131217.1; ncTRANSCRIPT 4 3927812; 3927814; (50%); 3927818; 3927819; INTERGENIC (50%); Block_5170 0.027069 chr3: 141596514 . . . 141622381; + 1 ATP1B3; INTRONIC 6 2645770; 2645771; (100%); 2645775; 2645776; 2645777; 2645780; Block_1940 0.027465 chr12: 110720638 . . . 110723521; + 1 ATP2A2; INTRONIC 2 3431489; 3431491; (100%); Block_5676 0.027465 chr5: 29476852 . . . 29477004; − 0 INTERGENIC 2 2851724; 2851725; (100%); Block_6897 0.027465 chr8: 22570904 . . . 22582442; − 1 PEBP4; CODING 2 3127612; 3127614; (100%); Block_1730 0.027867 chr12: 103238114 . . . 103246723; − 1 PAH; CODING 3 3468493; 3468497; (100%); 3468501; Block_1770 0.027867 chr12: 118597975 . . . 118610428; − 1 TAOK3; CODING 2 3473817; 3473823; (100%); Block_5708 0.027867 chr5: 55243448 . . . 55246076; − 1 IL6ST; CODING 4 2857431; 2857432; (25%); 2857433; 2857435; ncTRANSCRIPT (25%); INTRONIC (50%); Block_7242 0.027867 chr9: 94180062 . . . 94184577; − 1 NFIL3; INTRONIC 2 3214459; 3214464; (100%); Block_1097 0.028274 chr10: 93753461 . . . 93756275; + 1 BTAF1; CODING 3 3257988; 3257990; (100%); 3257991; Block_1270 0.028274 chr11: 64536711 . . . 64540977; − 1 SF1; CODING 3 3377068; 3377069; (100%); 3377075; Block_1523 0.028274 chr11: 114028398 . . . 114028592; + 1 ZBTB16; INTRONIC 2 3349769; 3349770; (100%); Block_2882 0.028274 chr16: 56968915 . . . 56970561; + 1 HERPUD1; INTRONIC 3 3662392; 3662394; (100%); 3662396; Block_2912 0.028274 chr16: 69727019 . . . 69727890; + 1 NFAT5; CODING 3 3666854; 3666855; (100%); 3666860; Block_5233 0.028274 chr3: 186790651 . . . 186795948; + 1 ST6GAL1; CODING 5 2656865; 2656867; (80%); UTR 2656868; 2656869; (20%); 2656870; Block_5487 0.028274 chr4: 40104120 . . . 40104817; + 1 N4BP2; CODING 2 2724618; 2724619; (100%); Block_7363 0.028274 chr9: 140350912 . . . 140350938; − 1 NELF; CODING 2 3231002; 4051780; (100%); Block_7688 0.028274 chrX: 138158562 . . . 138160882; − 1 FGF13; INTRONIC 2 4024012; 4023960; (100%); Block_7633 0.028686 chrX: 76938144 . . . 76938170; − 1 ATRX; CODING 2 4013275; 4055301; (100%); Block_1337 0.029104 chr11: 111779401 . . . 111782388; − 1 CRYAB; CODING 4 3391171; 3391173; (75%); UTR 3391176; 3391181; (25%); Block_3757 0.029104 chr2: 38976048 . . . 38976240; − 1 SRSF7; UTR (100%); 2 2548987; 2548988; Block_5217 0.029104 chr3: 182987375 . . . 182988389; + 1 B3GNT5; CODING 4 2654979; 2654980; (75%); UTR 2654981; 2654983; (25%); Block_7420 0.029104 chr9: 72912918 . . . 72915067; + 1 SMC5; CODING 2 3174237; 3174238; (100%); Block_7630 0.029104 chrX: 76912053 . . . 76912120; − 1 ATRX; CODING 2 4013266; 4055308; (100%); Block_2316 0.029526 chr14: 52794058 . . . 52794156; + 1 PTGER2; CODING 2 3535798; 3535799; (100%); Block_2728 0.029526 chr16: 28123180 . . . 28123325; − 1 XPO6; CODING 2 3686351; 3686352; (100%); Block_2900 0.029526 chr16: 68155896 . . . 68160503; + 1 NFATC3; CODING 5 3666049; 3666050; (100%); 3666052; 3666053; 3666055; Block_5704 0.029526 chr5: 54721975 . . . 54822340; − 1 PPAP2A; CODING 25 2857212; 2857213; (4%); 2857218; 2857219; INTRONIC 2857221; 2857222; (96%); 2857224; 2857226; 2857227; 2857231; 2857232; 2857238; 2857240; 2857241; 2857243; 2857244; 2857269; 2857271; 2857277; 2857284; 2857267; 2857247; 2857248; 2857249; 2857250; Block_5989 0.029526 chr5: 113698875 . . . 113699698; + 1 KCNN2; CODING 2 2824632; 2824635; (100%); Block_6904 0.029526 chr8: 27317314 . . . 27336535; − 1 CHRNA2; CODING 10 3129025; 3129030; (60%); UTR 3129034; 3129038; (40%); 3129039; 3129040; 3129044; 3129045; 3129046; 3129047; Block_2245 0.029954 chr14: 76424744 . . . 76448197; − 1 TGFB3; INTERGENIC 11 3572518; 3572524; (9.09%); 3572528; 3572529; CODING 3572533; 3572534; (45.45%); UTR 3572539; 3572540; (45.45%); 3572541; 3572542; 3572543; Block_6439 0.029954 chr6: 80383340 . . . 80406282; + 1 SH3BGRL2; CODING 2 2914706; 2914708; (100%); Block_6719 0.029954 chr7: 12620691 . . . 12691507; + 1 SCIN; CODING 9 2990415; 2990418; (100%); 2990420; 2990421; 2990424; 2990425; 2990427; 2990430; 2990431; Block_1375 0.030387 chr11: 134022430 . . . 134095174; − 1 NCAPD3; CODING 42 3399550; 3399551; (90.47%); UTR 3399553; 3399555; (7.14%); 3399562; 3399563; INTRONIC 3399565; 3399566; (2.38%); 3399567; 3399569; 3399570; 3399571; 3399572; 3399573; 3399574; 3399576; 3399577; 3399579; 3399580; 3399581; 3399583; 3399584; 3399585; 3399587; 3399588; 3399589; 3399590; 3399591; 3399592; 3399593; 3399594; 3399595; 3399597; 3399598; 3399600; 3399601; 3399602; 3399603; 3399605; 3399606; 3399607; 3399613; Block_2444 0.030387 chr15: 42730835 . . . 42737120; − 1 ZFP106; CODING 3 3620619; 3620620; (100%); 3620629; Block_3525 0.030387 chr19: 23543094 . . . 23545314; − 1 ZNF91; CODING 2 3857111; 3857120; (100%); Block_3864 0.030387 chr2: 121989436 . . . 121995260; − 1 TFCP2L1; CODING 3 2573607; 2573609; (100%); 2573613; Block_5724 0.030387 chr5: 59683251 . . . 59770534; − 1 PDE4D; INTRONIC 9 2858550; 2858561; (100%); 2858551; 2858552; 2858565; 2858431; 2858567; 2858575; 2858577; Block_2138 0.030825 chr13: 99098380 . . . 99099024; + 1 FARP1; CODING 2 3498035; 3498037; (100%); Block_2878 0.030825 chr16: 56642626 . . . 56643147; + 1 MT2A; INTRONIC 3 3662111; 3662112; (100%); 3662115; Block_6479 0.030825 chr6: 144070122 . . . 144075017; + 1 PHACTR2; CODING 2 2928962; 2928964; (100%); Block_1627 0.031269 chr12: 26755308 . . . 26755636; − 1 ITPR2; CODING 2 3448289; 3448290; (100%); Block_3754 0.031269 chr2: 38973291 . . . 38973876; − 1 SRSF7; CODING 2 2548976; 2548978; (100%); Block_5751 0.031269 chr5: 90667505 . . . 90675837; − 1 ARRDC3; ncTRANSCRIPT 6 2866739; 2866710; (33.33%); 2866715; 2866719; INTRONIC 2866723; 2866741; (66.66%); Block_612 0.031269 chr1: 110211967 . . . 110214138; + 1 GSTM2; CODING 4 2350963; 2350964; (100%); 2350971; 2350973; Block_6189 0.031269 chr6: 56479851 . . . 56507576; − 1 DST; CODING 27 2958476; 2958479; (96.29%); UTR 2958484; 2958485; (3.70%); 2958486; 2958487; 2958488; 2958489; 2958490; 2958491; 2958493; 2958494; 2958496; 2958497; 2958498; 2958500; 2958501; 2958502; 2958505; 2958506; 2958507; 2958508; 2958509; 2958510; 2958511; 2958512; 2958513; Block_906 0.031269 chr10: 64988219 . . . 65015457; − 1 JMJD1C; INTRONIC 4 3291839; 3291736; (100%); 3291737; 3291741; Block_1499 0.031719 chr11: 82878465 . . . 82878887; + 1 PCF11; CODING 2 3342544; 3342545; (100%); Block_2187 0.031719 chr14: 30374876 . . . 30385713; − 1 PRKD1; INTRONIC 2 3559283; 3559284; (100%); Block_3198 0.031719 chr17: 32583269 . . . 32584108; + 1 CCL2; CODING 3 3718173; 3718175; (66.66%); UTR 3718176; (33.33%); Block_4897 0.031719 chr3: 120370215 . . . 120370855; − 1 HGD; INTRONIC 2 2691428; 4047088; (100%); Block_6093 0.031719 chr6: 3270435 . . . 3287296; − 1 SLC22A23; CODING 4 2939302; 2939303; (50%); UTR 2939307; 2939313; (50%); Block_6632 0.031719 chr7: 95215175 . . . 95216702; − 1 PDK4; INTRONIC 2 3062085; 3062088; (100%); Block_2121 0.032173 chr13: 76379046 . . . 76379380; + 1 LMO7; INTRONIC 2 3494196; 3494197; (100%); Block_460 0.032173 chr1: 19981582 . . . 19984800; + 1 NBL1; CODING 3 2323777; 2323778; (66.66%); UTR 2323782; (33.33%); Block_5035 0.032173 chr3: 19190143 . . . 19190250; + 1 KCNH8; CODING 2 2613294; 2613295; (50%); UTR (50%); Block_5642 0.032173 chr4: 166301254 . . . 166375499; + 1 CPE; CODING 16 2750634; 2750635; (6.25%); UTR 2750636; 2750638; (12.5%); 2750639; 2750640; INTRONIC 2750642; 2750643; (81.25%); 2750680; 2750646; 2750647; 2750649; 2750650; 2750653; 2750655; 2750659; Block_5909 0.032173 chr5: 56526692 . . . 56531821; + 1 GPBP1; CODING 2 2810484; 2810487; (100%); Block_7744 0.032173 chrX: 23803557 . . . 23803771; + 1 SAT1; ncTRANSCRIPT 2 3971823; 3971825; (50%); INTRONIC (50%); Block_4816 0.032634 chr3: 64666890 . . . 64672644; − 1 ADAMTS9; CODING 4 2680160; 2680168; (100%); 2680170; 2680172; Block_5167 0.032634 chr3: 140251178 . . . 140275496; + 1 CLSTN2; CODING 2 2645167; 2645174; (100%); Block_5713 0.032634 chr5: 58442688 . . . 58450083; − 1 PDE4D; INTRONIC 3 2858190; 2858192; (100%); 2858194; Block_5967 0.032634 chr5: 96215443 . . . 96222457; + 1 ERAP2; CODING 2 2821370; 2821373; (100%); Block_2001 0.0331 chr13: 45048688 . . . 45053829; − 1 TSC22D1; INTRONIC 2 3512320; 3512324; (100%); Block_2049 0.0331 chr13: 95873854 . . . 95889452; − 1 ABCC4; INTRONIC 5 3521282; 3521283; (100%); 3521284; 3521286; 3521293; Block_6693 0.0331 chr7: 151864248 . . . 151873818; − 1 MLL3; CODING 4 3080082; 3080086; (100%); 3080088; 3080089; Block_7514 0.0331 chr9: 136333151 . . . 136333198; + 1 C9orf7; INTRONIC 2 3193029; 4050936; (100%); Block_127 0.033572 chr1: 25573295 . . . 25573974; − 1 C1orf63; CODING 3 2402129; 2402130; (33.33%); UTR 2402134; (66.66%); Block_4749 0.033572 chr3: 18427936 . . . 18438764; − 1 SATB1; CODING 3 2665227; 2665231; (100%); 2665233; Block_5231 0.033572 chr3: 186760464 . . . 186769107; + 1 ST6GAL1; CODING 3 2656855; 2656857; (66.66%); UTR 2656858; (33.33%); Block_6269 0.033572 chr6: 132617405 . . . 132618041; − 1 MOXD1; UTR (100%); 2 2974428; 2974429; Block_852 0.033572 chr10: 18837090 . . . 18840876; − 1 NSUN6; CODING 2 3280249; 3280253; (100%); Block_2202 0.034049 chr14: 50296082 . . . 50298964; − 1 NEMF; CODING 3 3563511; 3563512; (100%); 3563514; Block_3855 0.034049 chr2: 106002513 . . . 106013154; − 1 FHL2; CODING 2 2568717; 2568725; (50%); INTRONIC (50%); Block_4103 0.034049 chr2: 61333740 . . . 61335484; + 1 KIAA1841; CODING 2 2484488; 2484489; (100%); Block_4837 0.034049 chr3: 71622652 . . . 71629752; − 2 RP11- ncTRANSCRIPT 2 2682247; 2682249; 154H23.1; (50%); FOXP1; INTRONIC (50%); Block_6424 0.034049 chr6: 71125002 . . . 71264155; + 2 RNU7- ncTRANSCRIPT 30 2912782; 2912787; 48P; (6.66%); 2912788; 2912795; FAM135A; CODING 2912802; 2912803; (63.33%); UTR 2912806; 2912808; (3.33%); 2912809; 2912813; INTRONIC 2912814; 2912815; (26.66%); 2912816; 2912817; 2912818; 2912819; 2912820; 2912822; 2912824; 2912828; 2912829; 2912831; 2912832; 2912833; 2912838; 2912839; 2912841; 2912842; 2912847; 2912849; Block_6453 0.034049 chr6: 108938446 . . . 108942121; + 1 FOXO3; INTRONIC 2 2920510; 2920512; (100%); Block_1077 0.034532 chr10: 77453352 . . . 77454380; + 1 C10orf11; INTRONIC 2 3252742; 3252954; (100%); Block_1250 0.034532 chr11: 61295389 . . . 61300540; − 1 SYT7; CODING 2 3375406; 3375409; (100%); Block_6190 0.034532 chr6: 56503045 . . . 56504056; − 1 DST; ncTRANSCRIPT 2 2958503; 2958504; (50%); INTRONIC (50%); Block_6677 0.034532 chr7: 136935982 . . . 136938338; − 1 PTN; CODING 2 3074872; 3074873; (100%); Block_911 0.034532 chr10: 70276866 . . . 70276996; − 1 SLC25A16; UTR (100%); 2 3292763; 3292764; Block_993 0.034532 chr10: 118687375 . . . 118704523; − 1 KIAA1598; CODING 2 3308529; 3308533; (100%); Block_3168 0.035021 chr17: 7945688 . . . 7951882; + 1 ALOX15B; CODING 11 3709424; 3709426; (100%); 3709428; 3709429; 3709430; 3709432; 3709433; 3709435; 3709437; 3709438; 3709440; Block_3739 0.035021 chr2: 31749837 . . . 31754527; − 1 SRD5A2; ncTRANSCRIPT 3 2547235; 2547237; (100%); 2547238; Block_3049 0.035516 chr17: 56492694 . . . 56494638; − 1 RNF43; INTERGENIC 4 3764435; 3764437; (25%); 3764438; 3764441; CODING (25%); UTR (50%); Block_5576 0.035516 chr4: 106474899 . . . 106477521; + 1 ARHGEF38; INTRONIC 4 2738247; 2738268; (100%); 2738270; 2738248; Block_5770 0.035516 chr5: 98224781 . . . 98231958; − 1 CHD1; CODING 4 2868574; 2868577; (100%); 2868578; 2868580; Block_7247 0.035516 chr9: 95146567 . . . 95155495; − 1 OGN; CODING 6 3214802; 3214803; (50%); UTR 3214804; 3214806; (50%); 3214807; 3214810; Block_7810 0.035516 chrX: 105153170 . . . 105156727; + 1 NRK; CODING 2 3986120; 3986121; (100%); Block_1289 0.036017 chr11: 70824339 . . . 70830068; − 1 SHANK2; CODING 2 3380586; 3380591; (100%); Block_2429 0.036017 chr15: 37195097 . . . 37210290; − 1 MEIS2; INTRONIC 3 3618360; 3618366; (100%); 3618367; Block_2493 0.036017 chr15: 60677881 . . . 60688620; − 1 ANXA2; INTRONIC 9 3627332; 3627334; (100%); 3627336; 3627341; 3627343; 3627345; 3627346; 3627348; 3627349; Block_3679 0.036017 chr19: 51359727 . . . 51362135; + 1 KLK3; UTR (50%); 2 3839545; 3839552; INTRONIC (50%); Block_6037 0.036017 chr5: 145843146 . . . 145843355; + 1 TCERG1; CODING 2 2834115; 2834117; (100%); Block_7373 0.036017 chr9: 140361786 . . . 140361907; − 1 PNPLA7; CODING 2 3231040; 4051817; (100%); Block_2894 0.036524 chr16: 67159862 . . . 67178779; + 1 C16orf70; CODING 6 3665168; 3665171; (100%); 3665173; 3665177; 3665179; 3665183; Block_3987 0.036524 chr2: 216226027 . . . 216299511; − 1 FN1; CODING 57 2598267; 2598268; (94.73%); UTR 2598269; 2598270; (5.26%); 2598271; 2598273; 2598276; 2598277; 2598280; 2598281; 2598284; 2598286; 2598288; 2598289; 2598290; 2598294; 2598296; 2598299; 2598301; 2598302; 2598304; 2598306; 2598307; 2598308; 2598310; 2598313; 2598314; 2598318; 2598321; 2598324; 2598325; 2598328; 2598329; 2598330; 2598331; 2598334; 2598335; 2598338; 2598339; 2598340; 2598342; 2598344; 2598346; 2598352; 2598353; 2598354; 2598356; 2598357; 2598358; 2598360; 2598362; 2598363; 2598367; 2598371; 2598372; 2598373; 2598374; Block_416 0.036524 chr1: 235712540 . . . 235715511; − 1 GNG4; CODING 4 2461942; 2461944; (25%); UTR 2461945; 2461946; (75%); Block_7241 0.036524 chr9: 94171357 . . . 94172980; − 1 NFIL3; CODING 3 3214452; 3214453; (33.33%); UTR 3214454; (66.66%); Block_1192 0.037037 chr11: 8132291 . . . 8148335; − 1 RIC3; CODING 2 3361638; 3361639; (100%); Block_1431 0.037037 chr11: 35226060 . . . 35227773; + 1 CD44; CODING 2 3326700; 3326705; (100%); Block_1945 0.037037 chr12: 111558155 . . . 111620438; + 1 CUX2; INTRONIC 3 3431789; 3431792; (100%); 3431795; Block_2436 0.037037 chr15: 42437997 . . . 42439930; − 1 PLA2G4F; CODING 3 3620436; 3620439; (100%); 3620441; Block_6629 0.037037 chr7: 92354966 . . . 92355105; − 1 CDK6; CODING 2 3061361; 3061362; (100%); Block_7095 0.037037 chr8: 42798476 . . . 42805590; + 1 HOOK3; CODING 2 3096385; 3096387; (100%); Block_3287 0.037557 chr17: 65941696 . . . 65941965; + 1 BPTF; CODING 2 3732514; 3732516; (100%); Block_5073 0.037557 chr3: 42678445 . . . 42687432; + 1 NKTR; CODING 3 2619384; 2619390; (100%); 2619399; Block_4586 0.038082 chr21: 42541819 . . . 42601866; + 1 BACE2; INTRONIC 8 3921943; 3921944; (100%); 3921945; 3921949; 3921950; 3921951; 3921991; 3921961; Block_7687 0.038082 chrX: 138063436 . . . 138104840; − 1 FGF13; INTRONIC 4 4024021; 4024027; (100%); 4024008; 4024011; Block_7797 0.038082 chrX: 70782986 . . . 70784559; + 1 OGT; CODING 3 3981153; 3981154; (100%); 3981155; Block_7864 0.038082 chrY: 21903642 . . . 21905110; − 1 KDM5D; CODING 2 4036111; 4036113; (100%); Block_328 0.038614 chr1: 163112906 . . . 163122506; − 1 RGS5; CODING 7 2441391; 2441393; (42.85%); UTR 2441394; 2441395; (57.14%); 2441396; 2441398; 2441399; Block_5056 0.038614 chr3: 37356931 . . . 37360665; + 1 GOLGA4; CODING 2 2617089; 2617093; (100%); Block_7459 0.038614 chr9: 102590326 . . . 102590574; + 1 NR4A3; CODING 2 3181993; 3181994; (100%); Block_1339 0.039152 chr11: 115211747 . . . 115213046; − 1 CADM1; INTRONIC 2 3392448; 3392450; (100%); Block_1817 0.039152 chr12: 11805464 . . . 11817168; + 1 ETV6; INTRONIC 3 3405046; 3405051; (100%); 3405055; Block_6459 0.039152 chr6: 116431503 . . . 116431626; + 1 NT5DC1; INTRONIC 2 2922530; 2922531; (100%); Block_7222 0.039152 chr9: 74978264 . . . 74978497; − 1 ZFAND5; UTR (50%); 2 3209642; 3209643; INTRONIC (50%); Block_3275 0.039696 chr17: 59093209 . . . 59112144; + 1 BCAS3; CODING 2 3729624; 3729628; (100%); Block_6094 0.039696 chr6: 3304594 . . . 3307353; − 1 SLC22A23; INTRONIC 2 2939326; 2939328; (100%); Block_7372 0.039696 chr9: 140358830 . . . 140358908; − 1 PNPLA7; CODING 2 3231037; 4051814; (100%); Block_7611 0.039696 chrX: 73434306 . . . 73442101; − 0 INTERGENIC 2 4012764; 4012770; (100%); Block_260 0.040246 chr1: 120295908 . . . 120307209; − 1 HMGCS2; CODING 9 2431038; 2431042; (100%); 2431044; 2431047; 2431050; 2431051; 2431056; 2431057; 2431058; Block_7111 0.040246 chr8: 70570914 . . . 70572224; + 1 SULF1; UTR (100%); 2 3102461; 3102463; Block_1095 0.040803 chr10: 93722326 . . . 93723946; + 1 BTAF1; CODING 2 3257967; 3257969; (100%); Block_6228 0.040803 chr6: 99853979 . . . 99857124; − 1 SFRS18; CODING 2 2966275; 2966279; (100%); Block_1954 0.041367 chr12: 119631512 . . . 119632155; + 1 HSPB8; CODING 2 3434022; 3434023; (50%); UTR (50%); Block_2868 0.041367 chr16: 48395568 . . . 48396210; + 1 SIAH1; CODING_AS 3 3659376; 3659377; (100%); 3659378; Block_5186 0.041367 chr3: 156249230 . . . 156254535; + 1 KCNAB1; CODING 2 2649070; 2649077; (100%); Block_5326 0.041367 chr4: 80992745 . . . 80993659; − 1 ANTXR2; CODING 2 2775042; 2775043; (100%); Block_749 0.041367 chr1: 203276405 . . . 203277831; + 1 BTG2; CODING 3 2375671; 2375672; (33.33%); UTR 2375673; (66.66%); Block_186 0.041937 chr1: 59247791 . . . 59248778; − 1 JUN; CODING 2 2415092; 2415095; (50%); UTR (50%); Block_161 0.042514 chr1: 51768040 . . . 51768245; − 1 TTC39A; CODING 2 2412328; 2412330; (100%); Block_2076 0.042514 chr13: 24157611 . . . 24190183; + 1 TNFRSF19; CODING 5 3481424; 3481425; (60%); 3481429; 3481433; ncTRANSCRIPT 3481434; (20%); INTRONIC (20%); Block_3220 0.042514 chr17: 40932892 . . . 40945698; + 1 WNK4; CODING 8 3722087; 3722090; (100%); 3722094; 3722095; 3722100; 3722101; 3722105; 3722106; Block_3425 0.042514 chr18: 48581190 . . . 48586286; + 1 SMAD4; CODING 2 3788324; 3788330; (100%); Block_3684 0.042514 chr19: 52462246 . . . 52469039; + 1 AC011460.1; INTRONIC 4 3839986; 3839988; (100%); 3839990; 3839992; Block_4891 0.042514 chr3: 120351994 . . . 120352038; − 1 HGD; CODING 2 2691376; 4047113; (100%); Block_5619 0.042514 chr4: 148860985 . . . 148876520; + 1 ARHGAP10; CODING 3 2746763; 2746767; (100%); 2746769; Block_5894 0.042514 chr5: 38886367 . . . 38906492; + 1 OSMR; UTR (33.33%); 3 2807398; 2807399; INTRONIC 2807405; (66.66%); Block_3009 0.043098 chr17: 39079241 . . . 39084827; − 1 KRT23; CODING 4 3756593; 3756596; (100%); 3756602; 3756603; Block_5560 0.043098 chr4: 95507630 . . . 95508222; + 1 PDLIM5; CODING 3 2736395; 2736396; (33.33%); 2736397; INTRONIC (66.66%); Block_5893 0.043098 chr5: 38883930 . . . 38886253; + 1 OSMR; CODING 2 2807390; 2807396; (100%); Block_2154 0.043688 chr13: 111896260 . . . 111920011; + 1 ARHGEF7; CODING 2 3501707; 3501714; (100%); Block_2385 0.043688 chr14: 95081422 . . . 95084915; + 1 SERPINA3; ncTRANSCRIPT 3 3549773; 3549776; (100%); 3549777; Block_3557 0.043688 chr19: 52568528 . . . 52579356; − 1 ZNF841; CODING 4 3869431; 3869432; (100%); 3869434; 3869435; Block_3704 0.043688 chr19: 57802283 . . . 57804159; + 1 ZNF460; CODING 3 3843164; 3843166; (66.66%); UTR 3843168; (33.33%); Block_4020 0.043688 chr2: 239176702 . . . 239180131; − 1 PER2; CODING 2 2605780; 2605784; (100%); Block_6547 0.043688 chr6: 168272897 . . . 168281196; + 1 MLLT4; CODING 6 2936868; 4048405; (100%); 2936869; 4048403; 4048399; 2936875; Block_665 0.043688 chr1: 156100418 . . . 156106788; + 1 LMNA; CODING 7 2361313; 2361314; (100%); 2361316; 2361317; 2361320; 2361322; 2361325; Block_1064 0.044285 chr10: 71119734 . . . 71128378; + 1 HK1; CODING 2 3250324; 3250327; (100%); Block_4291 0.044285 chr2: 223758226 . . . 223772451; + 1 ACSL3; INTRONIC 2 2529553; 2529557; (100%); Block_5884 0.044285 chr5: 14602311 . . . 14607558; + 1 FAM105A; CODING 2 2802711; 2802714; (100%); Block_1047 0.044889 chr10: 51555733 . . . 51556843; + 1 MSMB; CODING 2 3246411; 3246412; (100%); Block_2056 0.044889 chr13: 107211047 . . . 107211667; − 1 ARGLU1; CODING 2 3524631; 3524633; (50%); INTRONIC (50%); Block_6885 0.044889 chr8: 17503466 . . . 17507465; − 1 MTUS1; CODING 3 3125921; 3125923; (100%); 3125925; Block_7779 0.044889 chrX: 53114856 . . . 53115271; + 1 TSPYL2; CODING 2 3978189; 3978190; (100%); Block_1046 0.0455 chr10: 51532298 . . . 51535286; + 2 TIMM23B; ncTRANSCRIPT 4 3246373; 3246408; RP11- (50%); 3246374; 3246376; 481A12.2; INTRONIC (50%); Block_3683 0.0455 chr19: 51380495 . . . 51381606; + 1 KLK2; INTRONIC 2 3839580; 3839583; (100%); Block_377 0.0455 chr1: 207102212 . . . 207112808; − 1 PIGR; CODING 11 2453007; 2453010; (90.90%); UTR 2453011; 2453012; (9.09%); 2453013; 2453015; 2453016; 2453018; 2453019; 2453020; 2453021; Block_4748 0.0455 chr3: 17413596 . . . 17425454; − 1 TBC1D5; CODING 5 2664953; 2664954; (100%); 2664955; 2664956; 2664957; Block_6405 0.0455 chr6: 44216514 . . . 44217722; + 1 HSP90AB1; INTRONIC 2 2908484; 2908490; (100%); Block_181 0.046118 chr1: 57025279 . . . 57038895; − 1 PPAP2B; INTRONIC 2 2414403; 2414411; (100%); Block_2341 0.046118 chr14: 64444642 . . . 64447421; + 1 SYNE2; CODING 2 3539761; 3539763; (100%); Block_2611 0.046118 chr15: 71574554 . . . 71586847; + 1 THSD4; INTRONIC 2 3600324; 3600327; (100%); Block_3692 0.046118 chr19: 54080729 . . . 54081190; + 1 ZNF331; CODING 2 3840996; 3840998; (100%); Block_7486 0.046118 chr9: 130914205 . . . 130914547; + 1 LCN2; CODING 2 3190204; 3190205; (100%); Block_1338 0.046743 chr11: 115099833 . . . 115111135; − 1 CADM1; CODING 3 3392393; 3392394; (100%); 3392398; Block_2668 0.046743 chr15: 99372148 . . . 99385603; + 1 IGF1R; INTRONIC 3 3610947; 3610951; (100%); 3610955; Block_2766 0.046743 chr16: 65005837 . . . 65022233; − 1 CDH11; CODING 3 3694677; 3694684; (100%); 3694691; Block_4086 0.046743 chr2: 46529640 . . . 46533141; + 1 EPAS1; INTRONIC 2 2480399; 2480401; (100%); Block_4584 0.046743 chr21: 40179160 . . . 40196766; + 1 ETS2; CODING 22 3921087; 3921088; (40.90%); UTR 3921089; 3921091; (18.18%); 3921092; 3921094; INTRONIC 3921096; 3921097; (40.90%); 3921098; 3921099; 3921100; 3921101; 3921102; 3921104; 3921105; 3921107; 3921109; 3921112; 3921115; 3921116; 3921118; 3921119; Block_5358 0.046743 chr4: 102196342 . . . 102200906; − 1 PPP3CA; INTRONIC 2 2779709; 2779739; (100%); Block_5575 0.046743 chr4: 106155858 . . . 106158231; + 1 TET2; CODING 2 2738167; 2738170; (100%); Block_5710 0.046743 chr5: 56219003 . . . 56219619; − 1 MIER3; CODING 2 2857736; 2857737; (100%); Block_1923 0.047375 chr12: 97945516 . . . 97949840; + 1 RMST; INTRONIC 2 3427537; 3427541; (100%); Block_2122 0.047375 chr13: 76395328 . . . 76397948; + 1 LMO7; CODING 2 3494214; 3494216; (100%); Block_2950 0.047375 chr17: 3743397 . . . 3746434; − 1 C17orf85; CODING 2 3741707; 3741708; (100%); Block_2090 0.048014 chr13: 31231614 . . . 31232191; + 1 USPL1; CODING 2 3484044; 3484045; (100%); Block_2246 0.048014 chr14: 76446944 . . . 76447361; − 1 TGFB3; CODING 2 3572536; 3572538; (50%); UTR (50%); Block_6421 0.048014 chr6: 64286908 . . . 64288684; + 1 PTP4A1; INTRONIC 3 2911920; 2911921; (100%); 2911925; Block_6888 0.048014 chr8: 18725208 . . . 18729431; − 1 PSD3; CODING 2 3126326; 3126328; (100%); Block_1477 0.048661 chr11: 66391897 . . . 66392352; + 1 RBM14; CODING 2 3336384; 3336386; (100%); Block_1662 0.048661 chr12: 52485769 . . . 52486601; − 0 INTERGENIC 2 3455115; 3455117; (100%); Block_171 0.048661 chr1: 53363109 . . . 53370744; − 1 ECHDC2; CODING 3 2413037; 2413040; (100%); 2413044; Block_6153 0.048661 chr6: 35623219 . . . 35655662; − 1 FKBP5; INTRONIC 7 2951608; 2951610; (100%); 2951614; 2951615; 2951616; 2951619; 2951627; Block_7713 0.048661 chrX: 2541426 . . . 2541450; + 1 CD99P1; ncTRANSCRIPT 2 3966810; 4028424; (100%); Block_875 0.048661 chr10: 33195427 . . . 33195769; − 1 ITGB1; CODING 2 3284196; 3284197; (50%); INTRONIC (50%); Block_934 0.048661 chr10: 79593681 . . . 79603456; − 1 DLG5; CODING 3 3296448; 3296449; (100%); 3296455; Block_1166 0.049314 chr10: 128816976 . . . 128817096; + 1 DOCK1; CODING 2 3269979; 3269980; (100%); Block_1946 0.049314 chr12: 111655706 . . . 111701632; + 1 CUX2; CODING 2 3431801; 3431809; (100%); Block_2243 0.049314 chr14: 75745675 . . . 75748413; − 1 FOS; CODING_AS 2 3572391; 3572392; (100%); Block_4019 0.049314 chr2: 239162223 . . . 239164537; − 1 PER2; CODING 2 2605759; 2605760; (100%); Block_4293 0.049314 chr2: 223781554 . . . 223782703; + 1 ACSL3; ncTRANSCRIPT 2 2529572; 2529573; (50%); INTRONIC (50%); Block_6218 0.049314 chr6: 90385836 . . . 90387413; − 1 MDN1; CODING 2 2964413; 2964414; (100%); Block_3693 0.049976 chr19: 54080311 . . . 54081259; + 1 ZNF331; CODING 2 3840995; 3840999; (50%); UTR (50%); Block_819 0.049976 chr1: 229242103 . . . 229242133; + 0 INTERGENIC 2 2384497; 4042435; (100%);

TABLE 24 SEQ ID NO.: Block ID Comparison Probe Set ID 293 Block_7113 BCR 3103710 297 Block_7113 BCR 3103707 300 Block_7113 BCR 3103712 303 Block_7113 BCR 3103708 309 Block_7113 BCR 3103706 311 Block_7113 BCR 3103713 312 Block_7113 BCR 3103715 316 Block_7113 BCR 3103704 481 Block_2879 BCR 3662122 482 Block_2879 BCR 3662124 483 Block_2879 BCR 3662156 484 Block_2879 BCR 3662163 485 Block_2922 GS 3670638 486 Block_2922 GS 3670639 487 Block_2922 GS 3670641 488 Block_2922 GS 3670644 489 Block_2922 GS 3670645 490 Block_2922 GS 3670650 491 Block_2922 GS 3670659 492 Block_2922 GS 3670660 493 Block_4271 GS 2528108 494 Block_4271 GS 2528110 495 Block_4271 GS 2528111 496 Block_4271 GS 2528112 497 Block_4271 GS 2528113 498 Block_4271 GS 2528115 499 Block_5000 GS 2608324 500 Block_5080 GS 2624393 501 Block_5080 GS 2624394 502 Block_5080 GS 2624395 503 Block_5080 GS 2624399 504 Block_5080 GS 2624416 505 Block_5080 GS 2624421 506 Block_5080 GS 2624427 507 Block_5080 GS 2624429 508 Block_5080 GS 2624453 509 Block_5080 GS 2624459 510 Block_5080 GS 2624460 511 Block_5080 GS 2624461 512 Block_5080 GS 2624462 513 Block_5080 GS 2624465 514 Block_5080 GS 2624466 515 Block_5080 GS 2624467 516 Block_5080 GS 2624470 517 Block_5080 GS 2624472 518 Block_5080 GS 2624473 519 Block_5080 GS 2624475 520 Block_5080 GS 2624477 521 Block_5080 GS 2624479 522 Block_5080 GS 2624480 523 Block_5080 GS 2624481 524 Block_5080 GS 2624482 525 Block_5080 GS 2624484 526 Block_5080 GS 2624485 527 Block_5080 GS 2624487 528 Block_5080 GS 2624488 529 Block_5080 GS 2624491 530 Block_5080 GS 2624494 531 Block_5080 GS 2624499 532 Block_5080 GS 2624500 533 Block_5080 GS 2624501 534 Block_5080 GS 2624502 535 Block_5080 GS 2624503 536 Block_5080 GS 2624504 537 Block_5080 GS 2624505 538 Block_5080 GS 2624507 539 Block_5080 GS 2624511 540 Block_5080 GS 2624515 541 Block_5080 GS 2624516 542 Block_5080 GS 2624518 543 Block_5080 GS 2624519 544 Block_5080 GS 2624526 545 Block_5470 BCR 2719689 546 Block_5470 BCR 2719692 547 Block_5470 BCR 2719694 548 Block_6371 BCR 2902713 549 Block_6371 BCR 2902730 550 Block_6592 BCR 3046457 551 Block_6592 BCR 3046459 552 Block_6592 BCR 3046460 553 Block_6592 BCR 3046461 554 Block_6592 BCR 3046462 555 Block_6592 BCR 3046465 556 Block_7113 BCR 3103714 557 Block_7113 BCR 3103717 558 Block_7716 GS 3970026 559 Block_7716 GS 3970034 560 Block_5470 BCR 2719696 561 Block_2922 GS 3670666 562 Block_4627 BCR 3956596 563 Block_4627 BCR 3956601 564 Block_5080 GS 2624397 565 Block_5080 GS 2624398 566 Block_5080 GS 2624400 567 Block_5080 GS 2624401 568 Block_5080 GS 2624402 569 Block_5080 GS 2624403 570 Block_5080 GS 2624404 571 Block_5080 GS 2624405 572 Block_5080 GS 2624406 573 Block_5080 GS 2624407 574 Block_5080 GS 2624408 575 Block_5080 GS 2624411 576 Block_5080 GS 2624412 577 Block_5080 GS 2624413 578 Block_5080 GS 2624415 579 Block_5080 GS 2624417 580 Block_5080 GS 2624422 581 Block_5080 GS 2624424 582 Block_5080 GS 2624426 583 Block_5080 GS 2624428 584 Block_5080 GS 2624432 585 Block_5080 GS 2624434 586 Block_5080 GS 2624435 587 Block_5080 GS 2624438 588 Block_5080 GS 2624439 589 Block_5080 GS 2624440 590 Block_5080 GS 2624441 591 Block_5080 GS 2624442 592 Block_5080 GS 2624443 593 Block_5080 GS 2624444 594 Block_5080 GS 2624446 595 Block_5080 GS 2624458 596 Block_5080 GS 2624490 597 Block_5080 GS 2624492 598 Block_5080 GS 2624493 599 Block_5080 GS 2624495 600 Block_5080 GS 2624496 601 Block_5080 GS 2624508 602 Block_5080 GS 2624512 603 Block_5080 GS 2624529 604 Block_5080 GS 2624531 605 Block_5080 GS 2624533 606 Block_5080 GS 2624537 607 Block_5155 BCR 2642733 608 Block_5155 BCR 2642735 609 Block_5155 BCR 2642740 610 Block_5155 BCR 2642741 611 Block_5155 BCR 2642744 612 Block_5155 BCR 2642745 613 Block_5155 BCR 2642746 614 Block_5155 BCR 2642747 615 Block_5155 BCR 2642748 616 Block_5155 BCR 2642750 617 Block_5155 BCR 2642753 618 Block_5000 GS 2608331 619 Block_5000 GS 2608332 620 Block_7716 GS 3970036 621 Block_7716 GS 3970039 622 Block_2879 BCR 3662175 623 Block_4627 BCR 3956603 624 Block_5080 GS 2624430 625 Block_5155 BCR 2642738 626 Block_5155 BCR 2642739 627 Block_2922 GS 3670661 628 Block_4271 GS 2528118 629 Block_5000 GS 2608321 630 Block_5000 GS 2608326 631 Block_5080 GS 2624389 632 Block_5080 GS 2624527 633 Block_5470 BCR 2719695 634 Block_6592 BCR 3046448 635 Block_6592 BCR 3046449 636 Block_6592 BCR 3046450 637 Block_7113 BCR 3103705 638 Block_7113 BCR 3103718 639 Block_7113 BCR 3103720 640 Block_7113 BCR 3103721 641 Block_7113 BCR 3103725 642 Block_7113 BCR 3103726

TABLE 25 Train Test Low Risk 13 12 Upgraded 16 15

TABLE 26 SEQ ID NO: Probe Set ID GENE SYMBOL DESCRIPTION 442 2343088 AK5 adenylate kinase 5 443 2476697 RASGRP3 RAS guanyl releasing protein 3 (calcium and DAG- regulated) 444 2518183 UBE2E3 ubiquitin-conjugating enzyme E2E 3 (UBC4/5 homolog, yeast) 445 2523351 BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) 446 2609586 RP11-58B17.1-015 447 2791421 FAM198B family with sequence similarity 198, member B 448 2825939 PRR16 proline rich 16 449 3018630 SLC26A4 solute carrier family 26, member 4 450 3046126 AOAH acyloxyacyl hydrolase (neutrophil) 451 3245912 WDFY4 WDFY family member 4 452 3331849 GLYATL1 glycine-N-acyltransferase-like 1 453 3332352 MS4A6E MS4A7 membrane-spanning 4-domains, subfamily A, member MS4A14 6E; 7; 14 454 3374811 AP000640.10 NA 455 3490910 OLFM4 olfactomedin 4 456 3490922 OLFM4 olfactomedin 4 457 4030108 USP9Y ubiquitin specific peptidase 9, Y-linked

TABLE 27 SEQ ID NO.: Probe Set ID Overlapping Gene 436 3454547 METTL7A 643 2351754 RP11-165H20.1 644 2352207 WNT2B 645 2425758 COL11A1 646 2425760 COL11A1 647 2439143 CD5L 648 2443478 SELE 649 2445999 ANGPTL1 650 2497104 IL1RL1; IL18R1 651 2537182 FAM150B 652 2557961 GKN2 653 2563801 AC096579.13; AC096579.7 654 2590074 ZNF385B 655 2597353 ACADL 656 2630510 ROBO2 657 2665784 ZNF385D 658 2690307 LSAMP 659 2690547 LSAMP; RP11-384F7.2 660 2735071 SPP1 661 2745931 HHIP 662 2745967 HHIP 663 2763608 PPARGC1A 664 2773359 665 2773360 PPBP 666 2877981 DNAJC18 667 2899180 HIST1H2BD 668 2931616 AKAP12 669 2992595 IL6 670 3010526 CD36 671 3039672 SOSTDC1 672 3066159 LHFPL3 673 3090264 ADAM28 674 3094812 TACC1 675 3094826 TACC1 676 3111647 PKHD1L1 677 3125131 DLC1 678 3127576 679 3128830 ADRA1A 680 3128833 ADRA1A 681 3142382 RP11-157I4.4 682 3142383 FABP4 683 3148249 RP11-152P17.2 684 3165878 TEK 685 3214804 OGN 686 3217691 NR4A3 687 3219225 KLF4 688 3248306 CDK1 689 3256240 AGAP11 690 3290059 PCDH15 691 3324452 FIBIN 692 3388860 MMP12 693 3388865 MMP12 694 3388870 MMP12 695 3388876 MMP12 696 3388879 MMP12 697 3420066 WIF1 698 3424154 699 3443978 700 3452294 SLC38A1 701 3461802 PTPRB 702 3489790 DLEU1 703 3517284 DACH1 704 3587566 GREM1 705 3589514 THBS1 706 3598183 AC069368.3; PLEKHO2 707 3620424 PLA2G4F 708 3624798 709 3629110 CSNK1G1; KIAA0101 710 3662123 MT1A 711 3716397 BLMH 712 3720984 TOP2A 713 3751793 SLC6A4 714 3763391 TMEM100 715 3834346 CEACAM5 716 3834373 CEACAM5 717 3834374 CEACAM5 718 3847635 RFX2 719 3847641 RFX2 720 3863109 ATP5SL 721 3863235 CEACAM5

TABLE 28 SEQ ID NO.: Probe Set ID Overlapping Gene 722 2325656 CLIC4 723 2340120 CACHD1 724 2343484 IFI44L 725 2370193 726 2372698 727 2425831 COL11A1 728 2432674 POLR3C 729 2451729 730 2464140 AKT3; RP11-370K11.1 731 2475754 LCLAT1 732 2477458 QPCT 733 2513024 734 2525590 MAP2 735 2525606 MAP2 736 2555049 BCL11A 737 2560264 AUP1 738 2570667 BUB1 739 2580618 LYPD6 740 2585026 SCN3A 741 2585470 SCN9A 742 2619699 SNRK 743 2643586 744 2647355 TM4SF4 745 2653664 KCNMB2 746 2654937 MCCC1 747 2658328 RP11-175P19.3 748 2658606 749 2685706 EPHA6 750 2700315 CPHL1P 751 2701244 MBNL1 752 2709053 IGF2BP2 753 2722908 PCDH7 754 2726525 OCIAD1 755 2730161 CSN1S1 756 2779454 DNAJB14 757 2794412 HPGD 758 2800740 ADCY2 759 2853946 760 2884854 GABRB2 761 2909786 C6orf141 762 2915096 763 2917256 764 2921416 SLC16A10 765 2925362 LAMA2 766 2933343 SNX9 767 2934286 768 2953202 RP1-278E11.5 769 2959207 LGSN 770 2959221 771 2977998 EPM2A 772 2982935 773 2983725 PACRG 774 2991528 HDAC9 775 2993670 CBX3 776 2996608 BMPER 777 3004356 ZNF679; RP11-3N2.13; RP11-3N2.1 778 3004687 ZNF138 779 3013087 COL1A2 780 3070073 FAM3C 781 3083209 CSMD1 782 3098089 ST18 783 3100188 RAB2A 784 3100290 CHD7 785 3105938 CPNE3 786 3106163 787 3118048 788 3124338 XKR6 789 3128057 790 3147448 UBR5 791 3153550 ASAP1 792 3154681 793 3194227 794 3241027 MAP3K8 795 3246418 TIMM23B; MSMB 796 3280411 C10orf112 797 3287743 RP11-463P17.1 798 3305180 COL17A1 799 3308634 PDZD8 800 3342551 PCF11 801 3393506 RP11-728F11.6; FXYD6

TABLE 29 SEQ ID NO.: Probe Set ID Overlapping Gene 653 2563801 AC096579.13; AC096579.7 663 2763608 PPARGC1A 685 3214804 OGN 802 2345084 CLCA4 803 2345093 CLCA4 804 2353490 ATP1A1 805 2374204 NR5A2 806 2451596 CHI3L1 807 2456712 SLC30A10 808 2490340 REG1A 809 2513937 B3GALT1 810 2513980 AC016723.4 811 2533060 UGT1A8; UGT1A10; UGT1A9 812 2563797 AC096579.13; AC096579.7 813 2563798 AC096579.13; AC096579.7 814 2594140 SATB2 815 2633196 CPOX 816 2635219 HHLA2 817 2730869 SLC4A4 818 2767399 ATP8A1 819 2772567 ENAM 820 2772569 IGJ 821 2772570 IGJ 822 2775911 PLAC8 823 2779235 ADH1B 824 2782578 CAMK2D 825 2872078 SEMA6A 826 2923919 PKIB 827 2974957 SLC2A12 828 2985814 THBS2 829 3018675 SLC26A3 830 3023440 AHCYL2 831 3039871 AGR3; RAD17P1 832 3047577 AC005027.3; INHBA 833 3062085 PDK4 834 3062104 PDK4 835 3090313 ADAMDEC1 836 3103850 HNF4G 837 3105612 CA2 838 3105614 CA2 839 3105622 CA2 840 3105629 CA2 841 3141870 TPD52 842 3142977 CA1 843 3142991 CA1 844 3163930 845 3165029 CDKN2B-AS1 846 3165030 CDKN2B-AS1 847 3174167 MAMDC2 848 3174519 GDA 849 3175362 PCSK5 850 3175465 PCSK5 851 3246960 PRKG1 852 3258838 NOC3L 853 3332433 MS4A12 854 3348424 C11orf93 855 3364272 RP11-396O20.2 856 3385068 SYTL2 857 3392098 FAM55D 858 3392111 FAM55D 859 3392128 860 3392143 861 3392145 862 3392151 863 3392154 864 3392167 865 3392170 866 3392175 867 3392180 868 3392181 869 3392189 870 3392191 871 3392197 872 3392211 873 3392215 874 3392223 875 3407503 PDE3A 876 3407520 PDE3A 877 3449955 878 3449956

TABLE 30 SEQ ID NO.: Probe Set ID Overlapping Gene 879 4012531 XIST 880 4012532 XIST 881 4012534 XIST 882 4012535 XIST 883 4012537 XIST 884 4012538 XIST 885 4012540 XIST 886 4012541 XIST 887 4012542 XIST 888 4012545 XIST 889 4012546 XIST 890 4012550 XIST 891 4012570 XIST 892 4012571 XIST 893 4012573 XIST 894 4012575 XIST 895 4012577 XIST 896 4012579 XIST 897 4012585 XIST 898 4012589 XIST 899 4012595 XIST 900 4012597 XIST 901 4012599 XIST 902 4030193 DDX3Y 903 4036117 KDM5D 

What is claimed is:
 1. A method of treating prostate cancer in a subject, comprising: (a) obtaining or having obtained an expression level in a sample from a subject for a plurality of targets, wherein the plurality of targets comprises at least two targets selected from the group consisting of SEQ ID NOs: 103, 218, 275, 301, 321, 607, 707, 795, 828 and 832; (b) determining that the subject is at risk of developing metastatic prostate cancer based on said expression level, or determining that the subject is not at risk of developing metastatic prostate cancer based on said expression level; and (c) administering a prostate cancer treatment to the subject if the subject is determined to be at risk of developing metastatic prostate cancer based on said expression level, or monitoring without administering the prostate cancer treatment to the subject if the subject is determined to not be at risk of developing metastatic prostate cancer based on said expression level.
 2. The method of claim 1, wherein the treatment is selected from the group consisting of surgery, a chemotherapeutic agent, radiation therapy, a biological therapeutic, cancer vaccine, gene therapy or a combination thereof.
 3. The method of claim 2, wherein the treatment is prostatectomy, a chemotherapeutic agent, anti-androgen therapy or a combination thereof.
 4. The method of claim 1, wherein the sample is selected from the group consisting of tissue, blood, plasma, serum, urine, a urine supernatant, a urine cell pellet, semen, prostatic secretions and prostate cells.
 5. The method of claim 1, wherein said obtaining comprises a method selected from the group consisting of a sequencing technique, a nucleic acid hybridization technique, and nucleic acid amplification technique, and an immunoassay.
 6. The method of claim 5, wherein the nucleic acid amplification technique is polymerase chain reaction, reverse transcription polymerase chain reaction, transcription-mediated amplification, ligase chain reaction, strand displacement amplification or nucleic acid sequence-based amplification.
 7. The method of claim 1, wherein administering a prostate cancer treatment comprises administering cisplatin, carboplatin, oxaliplatin, mechlorethamine, cyclophosphamide, chlorambucil, ifosfamide, vincristine, vinblastine, vinorelbine, vindesine, paclitaxel, docetaxel, podophyllotoxin, irinotecan, topotecan, amsacrine, etoposide, etoposide phosphate, teniposide, actinomycin, anthracyclines, doxorubicin, daunorubicin, valrubicin, idarubicin, epirubicin, bleomycin, plicamycin, mitomycin, tositumomab, samarium-153-lexidronam, strontium-89 chloride, interferon alpha, interferon beta, interferon gamma, interleukin-2, interleukin 7, interleukin 12, G-CSF, GM-CSF, rituximab, trastuzumab, bacillus Calmette-Guerin (BCG), levamisole, porfimer sodium, mitoxantrone, prednisone, samarium, strontium, finasteride, or dutasteride. 